As a Custom Software Engineer, you will engage in the development of custom software solutions that are designed to meet specific business needs. Your typical day will involve coding, enhancing components, and collaborating with team members to ensure the delivery of scalable and high-performing solutions using modern frameworks and agile methodologies. You will also participate in discussions to address challenges and contribute to the overall success of the projects you are involved in. Roles & Responsibilities: - Expected to perform independently and become an SME.- Required active participation/contribution in team discussions.- Contribute in providing solutions to work related problems.- Collaborate with cross-functional teams to gather requirements and translate them into technical specifications.- Conduct code reviews and provide constructive feedback to peers to ensure code quality and best practices. Professional & Technical Skills: - Must To Have Skills: Proficiency in ServiceNow IT Service Management.- Strong understanding of IT service management processes and best practices.- Experience with software development methodologies, particularly Agile and Scrum.- Familiarity with integration techniques and tools for ServiceNow.- Ability to troubleshoot and resolve technical issues efficiently
Responsibilities
As a Custom Software Engineer, you will engage in the development of custom software solutions that are designed to meet specific business needs. Your typical day will involve coding, enhancing components, and collaborating with team members to ensure the delivery of scalable and high-performing solutions using modern frameworks and agile methodologies. You will also participate in discussions to address challenges and contribute to the overall success of the projects you are involved in. Roles & Responsibilities: - Expected to perform independently and become an SME.- Required active participation/contribution in team discussions.- Contribute in providing solutions to work related problems.- Collaborate with cross-functional teams to gather requirements and translate them into technical specifications.- Conduct code reviews and provide constructive feedback to peers to ensure code quality and best practices. Professional & Technical Skills: - Must To Have Skills: Proficiency in ServiceNow IT Service Management.- Strong understanding of IT service management processes and best practices.- Experience with software development methodologies, particularly Agile and Scrum.- Familiarity with integration techniques and tools for ServiceNow.- Ability to troubleshoot and resolve technical issues efficiently
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
JoulestoWatts Business Solutions
Confidential · Internal Use OnlyPage
AI Consultant · Senior / Lead
Bangalore | Full-Time
6–12 Years · AI Strategy & Delivery · Enterprise AI Products
ROLE SUMMARY
You spend roughly equal parts of your time sitting with clients to understand what they actually need, designing AI solution architectures, and translating ambiguity into business cases, roadmaps, and delivery plans — while staying close enough to the technical work to lead through what you produce, not just through what you specify.
This role exists because most AI consultants we meet are either strong advisors who cannot architect, or strong engineers who cannot navigate a boardroom. We need someone who does both, and prefers it that way.
AI is the core of everything you will deliver. You will work across supervised ML, generative AI, LLM pipelines, RAG systems, and agentic workflows — bringing hands-on experience in Python, cloud platforms, and the broader AI/ML stack, alongside the consulting craft to make it land with clients.
The split is roughly 40% solutioning and client engagement, 60% delivery oversight and hands-on contribution. The exact balance flexes with the engagement.
WHAT YOU WILL DO
Client Engagement & Advisory
–
Engage with client stakeholders — CTOs, business sponsors, functional leads — to understand business problems, current-state processes, and decision-making workflows.
–
Conduct AI readiness assessments across data maturity, infrastructure, organisational capability, and process suitability for ML adoption.
–
Define and present AI solution roadmaps, use case prioritisation frameworks, and ROI models to senior stakeholders.
–
Translate complex AI concepts and model outputs into clear, business-relevant narratives for non-technical audiences.
–
Lead workshops, discovery sessions, and requirement-gathering exercises across cross-functional teams. The bar is to leave the room with a sharper problem statement than you walked in with.
Solution Architecture & Design
–
Design end-to-end AI solution architectures: data pipelines, model layer, inference services, and integration with existing enterprise systems.
–
Define data requirements, feature engineering strategies, and model evaluation criteria aligned to business objectives.
–
Assess build vs. buy vs. integrate options for AI components; provide structured recommendations with trade-off analysis.
–
Develop solution blueprints, architecture decision records, and technical specifications for delivery teams.
Delivery Oversight
–
Act as the technical bridge between client stakeholders and delivery and engineering teams throughout the project lifecycle.
–
Own solution quality: review model outputs, validate results against business acceptance criteria, and sign off on deployments.
–
Identify risks, dependencies, and scope changes; manage escalations and course corrections proactively.
–
Contribute to proof-of-concept development and prototype validation where required. You write code when it matters, not only when it is convenient.
JoulestoWatts Business Solutions
Confidential · Internal Use OnlyPage
Practice & Capability Development
–
Contribute to internal AI practice development: frameworks, reusable assets, methodology documentation, and proposal templates.
–
Support pre-sales activities: solution scoping, effort estimation, and proposal writing for AI engagements. You support the commercial motion; you do not own it.
–
Mentor junior consultants and analysts on both the technical and client-facing dimensions of AI delivery.
TECHNICAL SKILLS
This is the stack we expect you to be fluent in. Not every engagement uses every item, but a senior candidate will have hands-on experience across most of these.
AI & Machine Learning
–
Practical experience with supervised, unsupervised, and generative AI approaches applied to real business problems.
–
Model selection, evaluation, and trade-off analysis across regression, classification, and time-series tasks.
–
Explainability and responsible AI: bias assessment, confidence scoring, auditability.
–
Python ML stack: scikit-learn, XGBoost, PyTorch, or TensorFlow at production depth.
LLM & Generative AI
–
Applied LLM solutioning: RAG pipelines (chunking, embeddings, retrieval, re-ranking, evaluation), prompt engineering, structured output generation.
–
Agent frameworks: LangChain, LangGraph, or equivalent. MCP and tool calling.
–
Evaluating LLM fit vs. traditional ML for specific business use cases.
–
Governance and risk considerations for generative AI in enterprise environments.
Data & Architecture
–
Data architecture for AI systems: pipelines, feature engineering, storage, and integration patterns.
–
Cloud platform fluency: AWS, GCP, or Azure — including managed AI/ML services such as SageMaker, Vertex AI, or Azure ML.
–
API design and system integration concepts for embedding AI into enterprise workflows.
–
MLOps awareness: deployment, versioning, monitoring, and retraining cycles.
CONSULTING EXPERIENCE
We are not looking for someone who has advised from the sidelines. We are looking for someone who has owned delivery end-to-end.
–
You have run the full consulting cycle: discovery, solutioning, implementation oversight, and handover. You can walk through what you defined, what changed, and why.
–
You are comfortable in senior client conversations — CTOs, VPs, business sponsors — and know the difference between asking better questions and giving better answers.
–
You can probe a vague client request and surface the actual problem. Ambiguity is an opportunity, not a blocker.
–
You write well. One-page proposals, architecture documents, board-ready presentations, and concise stakeholder updates — with equal ease.
–
You have built business cases and ROI models that held up in procurement. You know what executives actually read in a slide deck.
–
You have worked in or alongside agile delivery teams and know when to move fast and when to slow down.
JoulestoWatts Business Solutions
Confidential · Internal Use OnlyPage
AI DELIVERY EXPERIENCE
We are not looking for a research scientist or someone who has only written notebooks. We are looking for someone who treats AI as production delivery.
–
You have shipped real AI solutions with users or business outcomes on the other end. You can name the systems, the stack, the failure modes, and what you fixed.
–
You understand the difference between a proof of concept and a production system. You have evaluation frameworks, observability, fallbacks, cost controls.
–
You exercise sound judgment on when AI helps and when it adds risk. You have recommended against AI-where-AI-does-not-fit and can explain why.
–
You are comfortable with the pace of the field. You read, test, and adapt. You do not require a stable spec to make progress.
–
You can explain LLM behaviour, model confidence, and AI limitations to a non-technical client without dumbing it down or overselling.
–
You have run pilots to demonstrate feasibility before full-scale delivery. You know how to design a pilot that answers the right question and builds stakeholder confidence — not just technical proof of concept.
–
Ideal candidate should have a product and microservice mindset — thinking in terms of composable, independently deployable components rather than monolithic solutions, and applying product discipline to how AI capabilities are scoped, iterated, and adopted.
CLIENT AND COMMUNICATION SKILLS
–
You have worked directly with external clients, not only internal stakeholders, at senior and executive levels.
–
You can run a discovery session and walk out with a sharper problem statement than when you walked in.
–
You write well and produce clean, concise deliverables: proposals, solution briefs, architecture documents, executive summaries.
–
You present technical work to non-technical audiences without losing them or condescending to them.
–
You navigate complex organisational dynamics and competing stakeholder interests without losing the plot.
LEADERSHIP
–
You have led delivery teams or mentored consultants and analysts through engagements. You know the difference between leading and managing.
–
You mentor others and can name specific people who grew under your guidance.
–
You set the bar through your own work and through the standards you hold others to in reviews and delivery checkpoints.
–
You are comfortable being the most senior technical voice in the room, and comfortable not being.
NICE TO HAVE
–
Domain experience in pricing, estimation, commercial operations, engineering services, or manufacturing.
–
Exposure to digital twin concepts, simulation models, or operations research applied to enterprise decisions.
–
Experience delivering AI in regulated or audit-sensitive environments.
–
Prior experience in a product company or AI platform business — not only services or consulting.
–
Familiarity with enterprise systems: ERP, CRM, CPQ, or PLM platforms as integration contexts.
JoulestoWatts Business Solutions
Confidential · Internal Use OnlyPage
–
Specific exposure to production agentic systems.
WHAT THIS ROLE IS NOT
–
Not a pure advisory role. If your last two years have been strategy decks without delivery accountability, this is not the right fit.
–
Not a pure engineering role. We need someone who leads through client relationships and delivery, not only through code.
–
Not a research role. We deliver production AI, not papers or experiments.
–
Not a pre-sales role. You support the commercial motion; you do not own it.
–
Not a project management role. You should understand what your team is delivering because you are close to the outputs, not because you are tracking a Gantt chart.
QUALIFICATIONS
–
Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related quantitative field.
–
6–12 years of experience spanning AI/ML engineering and client-facing consulting or solutioning roles.
–
Demonstrated track record of delivering AI solutions from problem definition through to production and business adoption.
–
Strong written and verbal communication skills; ability to produce board-ready presentations and detailed technical documents.
HOW WE WILL EVALUATE YOU
–
A portfolio of AI engagements or delivered solutions we can discuss in depth. We care about what you have actually shipped.
–
A technical conversation walking through one of your past AI projects: the problem, the approach, what broke, and what you would do differently.
–
A solutioning exercise where we hand you a vague client problem and watch you probe, scope, and propose. The exercise is graded on the questions you ask, not just the answer you produce.
–
A short paired session, live, on an AI-flavoured problem. We want to see how you think and work, not just what you can describe.
–
A reference conversation with a client, colleague, or team member who has seen you deliver.
Responsibilities
JoulestoWatts Business Solutions
Confidential · Internal Use OnlyPage
AI Consultant · Senior / Lead
Bangalore | Full-Time
6–12 Years · AI Strategy & Delivery · Enterprise AI Products
ROLE SUMMARY
You spend roughly equal parts of your time sitting with clients to understand what they actually need, designing AI solution architectures, and translating ambiguity into business cases, roadmaps, and delivery plans — while staying close enough to the technical work to lead through what you produce, not just through what you specify.
This role exists because most AI consultants we meet are either strong advisors who cannot architect, or strong engineers who cannot navigate a boardroom. We need someone who does both, and prefers it that way.
AI is the core of everything you will deliver. You will work across supervised ML, generative AI, LLM pipelines, RAG systems, and agentic workflows — bringing hands-on experience in Python, cloud platforms, and the broader AI/ML stack, alongside the consulting craft to make it land with clients.
The split is roughly 40% solutioning and client engagement, 60% delivery oversight and hands-on contribution. The exact balance flexes with the engagement.
WHAT YOU WILL DO
Client Engagement & Advisory
–
Engage with client stakeholders — CTOs, business sponsors, functional leads — to understand business problems, current-state processes, and decision-making workflows.
–
Conduct AI readiness assessments across data maturity, infrastructure, organisational capability, and process suitability for ML adoption.
–
Define and present AI solution roadmaps, use case prioritisation frameworks, and ROI models to senior stakeholders.
–
Translate complex AI concepts and model outputs into clear, business-relevant narratives for non-technical audiences.
–
Lead workshops, discovery sessions, and requirement-gathering exercises across cross-functional teams. The bar is to leave the room with a sharper problem statement than you walked in with.
Solution Architecture & Design
–
Design end-to-end AI solution architectures: data pipelines, model layer, inference services, and integration with existing enterprise systems.
–
Define data requirements, feature engineering strategies, and model evaluation criteria aligned to business objectives.
–
Assess build vs. buy vs. integrate options for AI components; provide structured recommendations with trade-off analysis.
–
Develop solution blueprints, architecture decision records, and technical specifications for delivery teams.
Delivery Oversight
–
Act as the technical bridge between client stakeholders and delivery and engineering teams throughout the project lifecycle.
–
Own solution quality: review model outputs, validate results against business acceptance criteria, and sign off on deployments.
–
Identify risks, dependencies, and scope changes; manage escalations and course corrections proactively.
–
Contribute to proof-of-concept development and prototype validation where required. You write code when it matters, not only when it is convenient.
JoulestoWatts Business Solutions
Confidential · Internal Use OnlyPage
Practice & Capability Development
–
Contribute to internal AI practice development: frameworks, reusable assets, methodology documentation, and proposal templates.
–
Support pre-sales activities: solution scoping, effort estimation, and proposal writing for AI engagements. You support the commercial motion; you do not own it.
–
Mentor junior consultants and analysts on both the technical and client-facing dimensions of AI delivery.
TECHNICAL SKILLS
This is the stack we expect you to be fluent in. Not every engagement uses every item, but a senior candidate will have hands-on experience across most of these.
AI & Machine Learning
–
Practical experience with supervised, unsupervised, and generative AI approaches applied to real business problems.
–
Model selection, evaluation, and trade-off analysis across regression, classification, and time-series tasks.
–
Explainability and responsible AI: bias assessment, confidence scoring, auditability.
–
Python ML stack: scikit-learn, XGBoost, PyTorch, or TensorFlow at production depth.
LLM & Generative AI
–
Applied LLM solutioning: RAG pipelines (chunking, embeddings, retrieval, re-ranking, evaluation), prompt engineering, structured output generation.
–
Agent frameworks: LangChain, LangGraph, or equivalent. MCP and tool calling.
–
Evaluating LLM fit vs. traditional ML for specific business use cases.
–
Governance and risk considerations for generative AI in enterprise environments.
Data & Architecture
–
Data architecture for AI systems: pipelines, feature engineering, storage, and integration patterns.
–
Cloud platform fluency: AWS, GCP, or Azure — including managed AI/ML services such as SageMaker, Vertex AI, or Azure ML.
–
API design and system integration concepts for embedding AI into enterprise workflows.
–
MLOps awareness: deployment, versioning, monitoring, and retraining cycles.
CONSULTING EXPERIENCE
We are not looking for someone who has advised from the sidelines. We are looking for someone who has owned delivery end-to-end.
–
You have run the full consulting cycle: discovery, solutioning, implementation oversight, and handover. You can walk through what you defined, what changed, and why.
–
You are comfortable in senior client conversations — CTOs, VPs, business sponsors — and know the difference between asking better questions and giving better answers.
–
You can probe a vague client request and surface the actual problem. Ambiguity is an opportunity, not a blocker.
–
You write well. One-page proposals, architecture documents, board-ready presentations, and concise stakeholder updates — with equal ease.
–
You have built business cases and ROI models that held up in procurement. You know what executives actually read in a slide deck.
–
You have worked in or alongside agile delivery teams and know when to move fast and when to slow down.
JoulestoWatts Business Solutions
Confidential · Internal Use OnlyPage
AI DELIVERY EXPERIENCE
We are not looking for a research scientist or someone who has only written notebooks. We are looking for someone who treats AI as production delivery.
–
You have shipped real AI solutions with users or business outcomes on the other end. You can name the systems, the stack, the failure modes, and what you fixed.
–
You understand the difference between a proof of concept and a production system. You have evaluation frameworks, observability, fallbacks, cost controls.
–
You exercise sound judgment on when AI helps and when it adds risk. You have recommended against AI-where-AI-does-not-fit and can explain why.
–
You are comfortable with the pace of the field. You read, test, and adapt. You do not require a stable spec to make progress.
–
You can explain LLM behaviour, model confidence, and AI limitations to a non-technical client without dumbing it down or overselling.
–
You have run pilots to demonstrate feasibility before full-scale delivery. You know how to design a pilot that answers the right question and builds stakeholder confidence — not just technical proof of concept.
–
Ideal candidate should have a product and microservice mindset — thinking in terms of composable, independently deployable components rather than monolithic solutions, and applying product discipline to how AI capabilities are scoped, iterated, and adopted.
CLIENT AND COMMUNICATION SKILLS
–
You have worked directly with external clients, not only internal stakeholders, at senior and executive levels.
–
You can run a discovery session and walk out with a sharper problem statement than when you walked in.
–
You write well and produce clean, concise deliverables: proposals, solution briefs, architecture documents, executive summaries.
–
You present technical work to non-technical audiences without losing them or condescending to them.
–
You navigate complex organisational dynamics and competing stakeholder interests without losing the plot.
LEADERSHIP
–
You have led delivery teams or mentored consultants and analysts through engagements. You know the difference between leading and managing.
–
You mentor others and can name specific people who grew under your guidance.
–
You set the bar through your own work and through the standards you hold others to in reviews and delivery checkpoints.
–
You are comfortable being the most senior technical voice in the room, and comfortable not being.
NICE TO HAVE
–
Domain experience in pricing, estimation, commercial operations, engineering services, or manufacturing.
–
Exposure to digital twin concepts, simulation models, or operations research applied to enterprise decisions.
–
Experience delivering AI in regulated or audit-sensitive environments.
–
Prior experience in a product company or AI platform business — not only services or consulting.
–
Familiarity with enterprise systems: ERP, CRM, CPQ, or PLM platforms as integration contexts.
JoulestoWatts Business Solutions
Confidential · Internal Use OnlyPage
–
Specific exposure to production agentic systems.
WHAT THIS ROLE IS NOT
–
Not a pure advisory role. If your last two years have been strategy decks without delivery accountability, this is not the right fit.
–
Not a pure engineering role. We need someone who leads through client relationships and delivery, not only through code.
–
Not a research role. We deliver production AI, not papers or experiments.
–
Not a pre-sales role. You support the commercial motion; you do not own it.
–
Not a project management role. You should understand what your team is delivering because you are close to the outputs, not because you are tracking a Gantt chart.
QUALIFICATIONS
–
Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related quantitative field.
–
6–12 years of experience spanning AI/ML engineering and client-facing consulting or solutioning roles.
–
Demonstrated track record of delivering AI solutions from problem definition through to production and business adoption.
–
Strong written and verbal communication skills; ability to produce board-ready presentations and detailed technical documents.
HOW WE WILL EVALUATE YOU
–
A portfolio of AI engagements or delivered solutions we can discuss in depth. We care about what you have actually shipped.
–
A technical conversation walking through one of your past AI projects: the problem, the approach, what broke, and what you would do differently.
–
A solutioning exercise where we hand you a vague client problem and watch you probe, scope, and propose. The exercise is graded on the questions you ask, not just the answer you produce.
–
A short paired session, live, on an AI-flavoured problem. We want to see how you think and work, not just what you can describe.
–
A reference conversation with a client, colleague, or team member who has seen you deliver.
Salary : Rs. 2,50,000.0 - Rs. 6,00,000.0
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
JoulestoWatts Business Solutions
Confidential · Internal Use Only
UX Engineer · Senior / Lead
Bangalore | Full-Time
6–12 Years · Product Design & Frontend Engineering · AI-Powered Enterprise Products
ROLE SUMMARY
You own the frontend of AI-powered enterprise products — translating product requirements into production-ready, accessible, and performant interfaces. This role sits at the intersection of design and engineering, and requires equal competency in both.
This role exists because most frontend engineers we meet either cannot design or cannot reason about the unique UX challenges of AI-driven products. We need someone who bridges that gap: who can implement a pixel-perfect component from a Figma spec in the morning and spend the afternoon defining how confidence scores and probabilistic outputs should behave in a human-in-the-loop review queue.
AI is woven through almost everything we ship. You will build interfaces for LLM outputs, model comparisons, explainability surfaces, and approval workflows — and you will bring the engineering discipline to make them fast, accessible, and production-grade.
The split is roughly 70% hands-on frontend engineering, 30% design collaboration and UX definition. The exact balance flexes with the product.
WHAT YOU WILL DO
Interface Development
–
Build production-grade React and TypeScript interfaces from design specifications, owning quality from component to deployment.
–
Develop and maintain a shared component library and design system used across multiple products and teams.
–
Implement complex, data-heavy dashboards with real-time updates, drill-downs, and configurable views.
–
Engineer multi-step workflows, configuration panels, and form-heavy interfaces with robust validation and error handling.
AI Product UX
–
Implement UI patterns for AI outputs: confidence scores, probability ranges, explanation panels, and suggestion overlays.
–
Build interfaces that support human-in-the-loop workflows: review queues, approval flows, override mechanisms, and audit logs.
–
Define API contracts with backend and ML engineers to optimise data delivery for frontend requirements. You know what you need and can specify it clearly.
Quality & Collaboration
–
Enforce WCAG 2.1 AA accessibility standards across all interfaces. Accessibility is not a checklist; it is a design constraint from the start.
–
Conduct usability testing with domain users; iterate based on findings, not assumptions.
–
Own frontend performance: Core Web Vitals, bundle optimisation, lazy loading, and caching strategies.
–
Lead code reviews, enforce coding standards, and mentor junior engineers through delivery.
TECHNICAL SKILLS
JoulestoWatts Business Solutions
Confidential · Internal Use Only
This is the stack we expect you to be fluent in. Not every product uses every item, but a senior candidate will have hands-on production experience across most of these.
Frontend Engineering
–
React — 5+ years in production: hooks, context, code splitting, performance optimisation.
–
TypeScript in strict mode, non-negotiable. You write types that help the next engineer, not ones that appease the compiler.
–
CSS: Tailwind CSS, CSS Modules, or styled-components at production depth.
–
State management: React Query, Zustand, or Redux Toolkit. You know when each is the right tool.
–
REST and GraphQL API integration; WebSocket for real-time feeds.
Data Visualisation
–
D3.js, Recharts, Nivo, or Plotly for custom charts and interactive graphs.
–
Virtualised lists and canvas rendering for large datasets without sacrificing responsiveness.
–
Visual representation of probabilistic outputs — confidence bands, distributions, uncertainty ranges.
UX Engineering
–
Translating Figma or Sketch designs into high-fidelity production interfaces without losing intent.
–
Designing form workflows: multi-step wizards, conditional logic, inline validation.
–
Strong grasp of information hierarchy and progressive disclosure in data-dense enterprise UIs.
ENGINEERING EXPERIENCE
We are looking for someone with 6 to 12 years of hands-on frontend and UX engineering in product environments. Beyond the stack, what matters:
–
You have personally delivered production interfaces end-to-end, more than once. You can walk through component architecture decisions, what broke in production, and what you would do differently.
–
You stay close to the code. Your last meaningful pull request was this week, not last quarter.
–
You design for the system that will exist in two years — a component library that scales, a design system that teams actually adopt — while also knowing when to stop designing and start shipping.
–
You are comfortable owning ambiguity. A vague product brief is an opportunity to ask better questions, not a blocker.
–
You read design files well, implement them cleanly, and push back constructively when something will not work at scale.
–
You have shipped in environments where the backend API was still being defined. You know how to move forward with mocks and align later.
AI PRODUCT EXPERIENCE
We are not looking for someone who has styled a chatbot interface. We are looking for someone who has thought deeply about how AI outputs should behave in a product.
–
You have shipped interfaces for AI-generated content or model outputs. You can name the product, the challenges, and the decisions you made.
–
You understand the difference between a demo interface and one that handles edge cases: empty states, low-confidence outputs, model errors, and latency.
–
You exercise sound judgment on when to expose AI uncertainty to the user and when to abstract it. You have made that call and can explain why.
–
You can work with ML engineers to understand what data is available and shape the frontend experience around realistic model outputs, not ideal ones.
JoulestoWatts Business Solutions
Confidential · Internal Use Only
NICE TO HAVE
–
Experience with AI product UIs: prompt-driven interfaces, model comparison views, or explainability surfaces.
–
Enterprise B2B product background: pricing, CPQ, ERP-adjacent, or analytics applications.
–
Ownership of or contribution to a multi-team shared design system.
–
Familiarity with micro-frontend architecture or module federation.
–
Domain exposure: engineering services, manufacturing, automotive, or similar technical industries.
WHAT THIS ROLE IS NOT
–
Not a pure design role. If your last two years have been Figma files without production code, this is not the right fit.
–
Not a pure engineering role. We need someone who can hold a design opinion and push back on patterns that will not serve users.
–
Not a marketing or consumer product role. This is enterprise software: data-dense, workflow-driven, and used by domain experts.
–
Not a delivery management role. You should know what the frontend is doing because you are reviewing the pull requests and writing a significant portion of the code.
QUALIFICATIONS
–
Bachelor’s or Master’s degree in Computer Science, Design, Human-Computer Interaction, or equivalent practical experience.
–
6–12 years of hands-on frontend and UX engineering in product environments.
–
Portfolio of shipped, production interfaces — enterprise or data-heavy applications preferred.
–
Strong written and verbal communication skills; ability to present design and engineering trade-offs to product and business stakeholders.
HOW WE WILL EVALUATE YOU
–
A portfolio of shipped interfaces we can discuss in depth. We care about decisions made, not just how things look.
–
A technical conversation walking through one of your past frontend projects: the architecture, what broke, and what you would do differently.
–
A design-to-code exercise where we hand you a Figma spec and an ambiguous brief. The exercise is graded on how you handle the gaps, not just whether the output renders.
–
A short live session on a small UI problem with AI output patterns. We want to see how you think in real time.
–
A reference conversation with someone who has worked with you on a shipped product.
Responsibilities
JoulestoWatts Business Solutions
Confidential · Internal Use Only
UX Engineer · Senior / Lead
Bangalore | Full-Time
6–12 Years · Product Design & Frontend Engineering · AI-Powered Enterprise Products
ROLE SUMMARY
You own the frontend of AI-powered enterprise products — translating product requirements into production-ready, accessible, and performant interfaces. This role sits at the intersection of design and engineering, and requires equal competency in both.
This role exists because most frontend engineers we meet either cannot design or cannot reason about the unique UX challenges of AI-driven products. We need someone who bridges that gap: who can implement a pixel-perfect component from a Figma spec in the morning and spend the afternoon defining how confidence scores and probabilistic outputs should behave in a human-in-the-loop review queue.
AI is woven through almost everything we ship. You will build interfaces for LLM outputs, model comparisons, explainability surfaces, and approval workflows — and you will bring the engineering discipline to make them fast, accessible, and production-grade.
The split is roughly 70% hands-on frontend engineering, 30% design collaboration and UX definition. The exact balance flexes with the product.
WHAT YOU WILL DO
Interface Development
–
Build production-grade React and TypeScript interfaces from design specifications, owning quality from component to deployment.
–
Develop and maintain a shared component library and design system used across multiple products and teams.
–
Implement complex, data-heavy dashboards with real-time updates, drill-downs, and configurable views.
–
Engineer multi-step workflows, configuration panels, and form-heavy interfaces with robust validation and error handling.
AI Product UX
–
Implement UI patterns for AI outputs: confidence scores, probability ranges, explanation panels, and suggestion overlays.
–
Build interfaces that support human-in-the-loop workflows: review queues, approval flows, override mechanisms, and audit logs.
–
Define API contracts with backend and ML engineers to optimise data delivery for frontend requirements. You know what you need and can specify it clearly.
Quality & Collaboration
–
Enforce WCAG 2.1 AA accessibility standards across all interfaces. Accessibility is not a checklist; it is a design constraint from the start.
–
Conduct usability testing with domain users; iterate based on findings, not assumptions.
–
Own frontend performance: Core Web Vitals, bundle optimisation, lazy loading, and caching strategies.
–
Lead code reviews, enforce coding standards, and mentor junior engineers through delivery.
TECHNICAL SKILLS
JoulestoWatts Business Solutions
Confidential · Internal Use Only
This is the stack we expect you to be fluent in. Not every product uses every item, but a senior candidate will have hands-on production experience across most of these.
Frontend Engineering
–
React — 5+ years in production: hooks, context, code splitting, performance optimisation.
–
TypeScript in strict mode, non-negotiable. You write types that help the next engineer, not ones that appease the compiler.
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CSS: Tailwind CSS, CSS Modules, or styled-components at production depth.
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State management: React Query, Zustand, or Redux Toolkit. You know when each is the right tool.
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REST and GraphQL API integration; WebSocket for real-time feeds.
Data Visualisation
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D3.js, Recharts, Nivo, or Plotly for custom charts and interactive graphs.
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Virtualised lists and canvas rendering for large datasets without sacrificing responsiveness.
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Visual representation of probabilistic outputs — confidence bands, distributions, uncertainty ranges.
UX Engineering
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Translating Figma or Sketch designs into high-fidelity production interfaces without losing intent.
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Designing form workflows: multi-step wizards, conditional logic, inline validation.
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Strong grasp of information hierarchy and progressive disclosure in data-dense enterprise UIs.
ENGINEERING EXPERIENCE
We are looking for someone with 6 to 12 years of hands-on frontend and UX engineering in product environments. Beyond the stack, what matters:
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You have personally delivered production interfaces end-to-end, more than once. You can walk through component architecture decisions, what broke in production, and what you would do differently.
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You stay close to the code. Your last meaningful pull request was this week, not last quarter.
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You design for the system that will exist in two years — a component library that scales, a design system that teams actually adopt — while also knowing when to stop designing and start shipping.
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You are comfortable owning ambiguity. A vague product brief is an opportunity to ask better questions, not a blocker.
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You read design files well, implement them cleanly, and push back constructively when something will not work at scale.
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You have shipped in environments where the backend API was still being defined. You know how to move forward with mocks and align later.
AI PRODUCT EXPERIENCE
We are not looking for someone who has styled a chatbot interface. We are looking for someone who has thought deeply about how AI outputs should behave in a product.
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You have shipped interfaces for AI-generated content or model outputs. You can name the product, the challenges, and the decisions you made.
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You understand the difference between a demo interface and one that handles edge cases: empty states, low-confidence outputs, model errors, and latency.
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You exercise sound judgment on when to expose AI uncertainty to the user and when to abstract it. You have made that call and can explain why.
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You can work with ML engineers to understand what data is available and shape the frontend experience around realistic model outputs, not ideal ones.
JoulestoWatts Business Solutions
Confidential · Internal Use Only
NICE TO HAVE
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Experience with AI product UIs: prompt-driven interfaces, model comparison views, or explainability surfaces.
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Enterprise B2B product background: pricing, CPQ, ERP-adjacent, or analytics applications.
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Ownership of or contribution to a multi-team shared design system.
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Familiarity with micro-frontend architecture or module federation.
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Domain exposure: engineering services, manufacturing, automotive, or similar technical industries.
WHAT THIS ROLE IS NOT
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Not a pure design role. If your last two years have been Figma files without production code, this is not the right fit.
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Not a pure engineering role. We need someone who can hold a design opinion and push back on patterns that will not serve users.
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Not a marketing or consumer product role. This is enterprise software: data-dense, workflow-driven, and used by domain experts.
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Not a delivery management role. You should know what the frontend is doing because you are reviewing the pull requests and writing a significant portion of the code.
QUALIFICATIONS
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Bachelor’s or Master’s degree in Computer Science, Design, Human-Computer Interaction, or equivalent practical experience.
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6–12 years of hands-on frontend and UX engineering in product environments.
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Portfolio of shipped, production interfaces — enterprise or data-heavy applications preferred.
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Strong written and verbal communication skills; ability to present design and engineering trade-offs to product and business stakeholders.
HOW WE WILL EVALUATE YOU
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A portfolio of shipped interfaces we can discuss in depth. We care about decisions made, not just how things look.
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A technical conversation walking through one of your past frontend projects: the architecture, what broke, and what you would do differently.
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A design-to-code exercise where we hand you a Figma spec and an ambiguous brief. The exercise is graded on how you handle the gaps, not just whether the output renders.
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A short live session on a small UI problem with AI output patterns. We want to see how you think in real time.
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A reference conversation with someone who has worked with you on a shipped product.
Salary : Rs. 1,50,000.0 - Rs. 3,00,000.0
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
