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
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.
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
Job Description:
Experience
• 10+ years overall experience in software engineering, data, AI/ML, or architecture
• 5+ years designing and delivering AI/ML and Generative AI solutions
• Experience leading enterprise-scale AI programs
Role Summary
Lead the architecture, governance, and delivery of enterprise AI solutions spanning Machine Learning, Predictive Analytics, Deep Learning, Generative AI, and Agentic AI. Define scalable, secure, and business-aligned AI platforms, applications, and standards.
Key Responsibilities
AI Architecture
• Design end-to-end AI/ML and GenAI solution architectures.
• Define AI reference architectures, standards, and best practices.
• Architect AI-powered applications using ML models, LLMs, RAG, copilots, and AI agents.
AI Platforms & Engineering
• Design AI platforms supporting MLOps, LLMOps, model deployment, monitoring, and governance.
• Establish reusable AI services, APIs, accelerators, and frameworks.
• Drive scalability, reliability, security, and cost optimization.
Data & Cloud
• Architect cloud-native AI solutions on AWS, Azure, or GCP.
• Design data pipelines, lakehouse, feature store, and vector database architectures.
• Integrate AI solutions with enterprise systems and business applications.
Governance & Leadership
• Define Responsible AI, security, compliance, and governance frameworks.
• Lead architecture reviews and mentor AI engineering teams.
• Partner with business stakeholders to identify and prioritize AI opportunities.
Required Skills
• Machine Learning, Deep Learning, NLP, Predictive Analytics
• LLMs, RAG, AI Agents, Prompt Engineering, Vector Databases
• Python, TensorFlow/PyTorch, LangChain/LangGraph, MLflow
• MLOps, LLMOps, Docker, Kubernetes, CI/CD
• AWS, Azure, or GCP
AI/ML Engineer
Experience
• 4–6 years of overall experience in software development, data engineering, AI/ML, or analytics
• 2+ years of hands-on experience in AI/ML and/or Generative AI solutions
Role Summary
Design, develop, and deploy AI-powered applications and solutions leveraging Machine Learning, Generative AI, and Agentic AI solutions. Work closely with architects, data scientists, and business stakeholders to build scalable, secure, and production-ready AI systems.
Key Responsibilities
AI Solution Development
• Develop and implement AI/ML, Generative AI & Agentic solutions for business use cases.
• Build applications using LLMs, RAG, AI Agents, copilots, and intelligent automation frameworks.
• Support model development, evaluation, deployment, and monitoring activities.
Data & AI Engineering
• Build and maintain data pipelines, embeddings pipelines, and vector databases.
• Prepare, transform, and validate data for AI/ML workloads.
• Integrate AI services with enterprise applications and APIs.
Platform & Operations
• Support MLOps and LLMOps processes including deployment, monitoring, and performance optimization.
• Develop reusable components, prompts, workflows, and AI accelerators.
• Contribute to AI platform enhancements and automation initiatives.
Collaboration & Innovation
• Work with cross-functional teams to define requirements and deliver AI solutions.
• Evaluate emerging AI technologies, models, and frameworks.
• Follow AI governance, security, and Responsible AI guidelines.
Required Skills
• Python and software development fundamentals
• Machine Learning, NLP, and data analytics concepts
• LLMs, Prompt Engineering, RAG, and AI Agent frameworks
• LangChain, LangGraph, LlamaIndex, or similar frameworks
• SQL, APIs, and data integration
• Docker, Git, CI/CD fundamentals
• AWS, Azure, or GCP exposure
Important Guidelines:
• Preliminary Evaluation By Partner - Overall assessment of profile from your side.
• Proficiency level on Topic - Candidates knowledge on particular skill
Responsibilities
10+ years overall experience in software engineering, data, AI/ML, or architecture
• 5+ years designing and delivering AI/ML and Generative AI solutions
• Experience leading enterprise-scale AI programs
Role Summary
Lead the architecture, governance, and delivery of enterprise AI solutions spanning Machine Learning, Predictive Analytics, Deep Learning, Generative AI, and Agentic AI. Define scalable, secure, and business-aligned AI platforms, applications, and standards.
Key Responsibilities
AI Architecture
• Design end-to-end AI/ML and GenAI solution architectures.
• Define AI reference architectures, standards, and best practices.
• Architect AI-powered applications using ML models, LLMs, RAG, copilots, and AI agents.
AI Platforms & Engineering
• Design AI platforms supporting MLOps, LLMOps, model deployment, monitoring, and governance.
• Establish reusable AI services, APIs, accelerators, and frameworks.
• Drive scalability, reliability, security, and cost optimization.
Data & Cloud
• Architect cloud-native AI solutions on AWS, Azure, or GCP.
• Design data pipelines, lakehouse, feature store, and vector database architectures.
• Integrate AI solutions with enterprise systems and business applications.
Governance & Leadership
• Define Responsible AI, security, compliance, and governance frameworks.
• Lead architecture reviews and mentor AI engineering teams.
• Partner with business stakeholders to identify and prioritize AI opportunities.
Required Skills
• Machine Learning, Deep Learning, NLP, Predictive Analytics
• LLMs, RAG, AI Agents, Prompt Engineering, Vector Databases
• Python, TensorFlow/PyTorch, LangChain/LangGraph, MLflow
• MLOps, LLMOps, Docker, Kubernetes, CI/CD
• AWS, Azure, or GCP
AI/ML Engineer
Experience
• 4–6 years of overall experience in software development, data engineering, AI/ML, or analytics
• 2+ years of hands-on experience in AI/ML and/or Generative AI solutions
Role Summary
Design, develop, and deploy AI-powered applications and solutions leveraging Machine Learning, Generative AI, and Agentic AI solutions. Work closely with architects, data scientists, and business stakeholders to build scalable, secure, and production-ready AI systems.
Key Responsibilities
AI Solution Development
• Develop and implement AI/ML, Generative AI & Agentic solutions for business use cases.
• Build applications using LLMs, RAG, AI Agents, copilots, and intelligent automation frameworks.
• Support model development, evaluation, deployment, and monitoring activities.
Data & AI Engineering
• Build and maintain data pipelines, embeddings pipelines, and vector databases.
• Prepare, transform, and validate data for AI/ML workloads.
• Integrate AI services with enterprise applications and APIs.
Platform & Operations
• Support MLOps and LLMOps processes including deployment, monitoring, and performance optimization.
• Develop reusable components, prompts, workflows, and AI accelerators.
• Contribute to AI platform enhancements and automation initiatives.
Collaboration & Innovation
• Work with cross-functional teams to define requirements and deliver AI solutions.
• Evaluate emerging AI technologies, models, and frameworks.
• Follow AI governance, security, and Responsible AI guidelines.
Required Skills
• Python and software development fundamentals
• Machine Learning, NLP, and data analytics concepts
• LLMs, Prompt Engineering, RAG, and AI Agent frameworks
• LangChain, LangGraph, LlamaIndex, or similar frameworks
• SQL, APIs, and data integration
• Docker, Git, CI/CD fundamentals
• AWS, Azure, or GCP exposure
Important Guidelines:
• Preliminary Evaluation By Partner - Overall assessment of profile from your side.
• Proficiency level on Topic - Candidates knowledge on particular skill
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
Job Description:
Experience
• 10+ years overall experience in software engineering, data, AI/ML, or architecture
• 5+ years designing and delivering AI/ML and Generative AI solutions
• Experience leading enterprise-scale AI programs
Role Summary
Lead the architecture, governance, and delivery of enterprise AI solutions spanning Machine Learning, Predictive Analytics, Deep Learning, Generative AI, and Agentic AI. Define scalable, secure, and business-aligned AI platforms, applications, and standards.
Key Responsibilities
AI Architecture
• Design end-to-end AI/ML and GenAI solution architectures.
• Define AI reference architectures, standards, and best practices.
• Architect AI-powered applications using ML models, LLMs, RAG, copilots, and AI agents.
AI Platforms & Engineering
• Design AI platforms supporting MLOps, LLMOps, model deployment, monitoring, and governance.
• Establish reusable AI services, APIs, accelerators, and frameworks.
• Drive scalability, reliability, security, and cost optimization.
Data & Cloud
• Architect cloud-native AI solutions on AWS, Azure, or GCP.
• Design data pipelines, lakehouse, feature store, and vector database architectures.
• Integrate AI solutions with enterprise systems and business applications.
Governance & Leadership
• Define Responsible AI, security, compliance, and governance frameworks.
• Lead architecture reviews and mentor AI engineering teams.
• Partner with business stakeholders to identify and prioritize AI opportunities.
Required Skills
• Machine Learning, Deep Learning, NLP, Predictive Analytics
• LLMs, RAG, AI Agents, Prompt Engineering, Vector Databases
• Python, TensorFlow/PyTorch, LangChain/LangGraph, MLflow
• MLOps, LLMOps, Docker, Kubernetes, CI/CD
• AWS, Azure, or GCP
AI/ML Engineer
Experience
• 4–6 years of overall experience in software development, data engineering, AI/ML, or analytics
• 2+ years of hands-on experience in AI/ML and/or Generative AI solutions
Role Summary
Design, develop, and deploy AI-powered applications and solutions leveraging Machine Learning, Generative AI, and Agentic AI solutions. Work closely with architects, data scientists, and business stakeholders to build scalable, secure, and production-ready AI systems.
Key Responsibilities
AI Solution Development
• Develop and implement AI/ML, Generative AI & Agentic solutions for business use cases.
• Build applications using LLMs, RAG, AI Agents, copilots, and intelligent automation frameworks.
• Support model development, evaluation, deployment, and monitoring activities.
Data & AI Engineering
• Build and maintain data pipelines, embeddings pipelines, and vector databases.
• Prepare, transform, and validate data for AI/ML workloads.
• Integrate AI services with enterprise applications and APIs.
Platform & Operations
• Support MLOps and LLMOps processes including deployment, monitoring, and performance optimization.
• Develop reusable components, prompts, workflows, and AI accelerators.
• Contribute to AI platform enhancements and automation initiatives.
Collaboration & Innovation
• Work with cross-functional teams to define requirements and deliver AI solutions.
• Evaluate emerging AI technologies, models, and frameworks.
• Follow AI governance, security, and Responsible AI guidelines.
Required Skills
• Python and software development fundamentals
• Machine Learning, NLP, and data analytics concepts
• LLMs, Prompt Engineering, RAG, and AI Agent frameworks
• LangChain, LangGraph, LlamaIndex, or similar frameworks
• SQL, APIs, and data integration
• Docker, Git, CI/CD fundamentals
• AWS, Azure, or GCP exposure
Important Guidelines:
• Preliminary Evaluation By Partner - Overall assessment of profile from your side.
• Proficiency level on Topic - Candidates knowledge on particular skill
Responsibilities
10+ years overall experience in software engineering, data, AI/ML, or architecture
• 5+ years designing and delivering AI/ML and Generative AI solutions
• Experience leading enterprise-scale AI programs
Role Summary
Lead the architecture, governance, and delivery of enterprise AI solutions spanning Machine Learning, Predictive Analytics, Deep Learning, Generative AI, and Agentic AI. Define scalable, secure, and business-aligned AI platforms, applications, and standards.
Key Responsibilities
AI Architecture
• Design end-to-end AI/ML and GenAI solution architectures.
• Define AI reference architectures, standards, and best practices.
• Architect AI-powered applications using ML models, LLMs, RAG, copilots, and AI agents.
AI Platforms & Engineering
• Design AI platforms supporting MLOps, LLMOps, model deployment, monitoring, and governance.
• Establish reusable AI services, APIs, accelerators, and frameworks.
• Drive scalability, reliability, security, and cost optimization.
Data & Cloud
• Architect cloud-native AI solutions on AWS, Azure, or GCP.
• Design data pipelines, lakehouse, feature store, and vector database architectures.
• Integrate AI solutions with enterprise systems and business applications.
Governance & Leadership
• Define Responsible AI, security, compliance, and governance frameworks.
• Lead architecture reviews and mentor AI engineering teams.
• Partner with business stakeholders to identify and prioritize AI opportunities.
Required Skills
• Machine Learning, Deep Learning, NLP, Predictive Analytics
• LLMs, RAG, AI Agents, Prompt Engineering, Vector Databases
• Python, TensorFlow/PyTorch, LangChain/LangGraph, MLflow
• MLOps, LLMOps, Docker, Kubernetes, CI/CD
• AWS, Azure, or GCP
AI/ML Engineer
Experience
• 4–6 years of overall experience in software development, data engineering, AI/ML, or analytics
• 2+ years of hands-on experience in AI/ML and/or Generative AI solutions
Role Summary
Design, develop, and deploy AI-powered applications and solutions leveraging Machine Learning, Generative AI, and Agentic AI solutions. Work closely with architects, data scientists, and business stakeholders to build scalable, secure, and production-ready AI systems.
Key Responsibilities
AI Solution Development
• Develop and implement AI/ML, Generative AI & Agentic solutions for business use cases.
• Build applications using LLMs, RAG, AI Agents, copilots, and intelligent automation frameworks.
• Support model development, evaluation, deployment, and monitoring activities.
Data & AI Engineering
• Build and maintain data pipelines, embeddings pipelines, and vector databases.
• Prepare, transform, and validate data for AI/ML workloads.
• Integrate AI services with enterprise applications and APIs.
Platform & Operations
• Support MLOps and LLMOps processes including deployment, monitoring, and performance optimization.
• Develop reusable components, prompts, workflows, and AI accelerators.
• Contribute to AI platform enhancements and automation initiatives.
Collaboration & Innovation
• Work with cross-functional teams to define requirements and deliver AI solutions.
• Evaluate emerging AI technologies, models, and frameworks.
• Follow AI governance, security, and Responsible AI guidelines.
Required Skills
• Python and software development fundamentals
• Machine Learning, NLP, and data analytics concepts
• LLMs, Prompt Engineering, RAG, and AI Agent frameworks
• LangChain, LangGraph, LlamaIndex, or similar frameworks
• SQL, APIs, and data integration
• Docker, Git, CI/CD fundamentals
• AWS, Azure, or GCP exposure
Important Guidelines:
• Preliminary Evaluation By Partner - Overall assessment of profile from your side.
• Proficiency level on Topic - Candidates knowledge on particular skill
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
"Skills Required -
1. Hands on experience on building real?time API inventory (managed, shadow, zombie/legacy) across clouds, gateways, ingress controllers, and services
2.Expertise on behavioral anomaly detection, abuse pattern detection (credential stuffing, BOLA/BFLA, scraping), and DDoS rate?limiting via platform policies and gateway integrations.
3. Map API controls to frameworks and regulations (e.g., ISO 27001, SOC 2, PCI DSS, HIPAA, GDPR).
4. Understanding of OWASP API Security Top 10, rate limiting, caching, idempotency, and back?pressure patterns.
5.Experience with OpenAPI/Swagger, schema governance, and contract testing; familiarity with GraphQL security.
6. Hands?on with Salt Security, 42Crunch, and Noname Security (deployment, policy creation, tuning, and integrations)
Responsibility –
1. Define the API security architecture and standards spanning discovery ? design review ? pre?prod testing ? runtime protection ? monitoring & response.
2. Establish reference architectures integrating Salt / 42Crunch / Noname with gateways (Apigee, Kong, Azure API Management), CI/CD, SIEM/SOAR, and IAM.
3. Establish and enforce API security standards, secure design patterns, and governance controls aligned with Zero?Trust and OWASP API Security principles.
4. Define API authentication and authorization models (OAuth2, OIDC, JWT, mTLS, token scoping, audience restrictions).
Responsibilities
"Skills Required -
1. Hands on experience on building real?time API inventory (managed, shadow, zombie/legacy) across clouds, gateways, ingress controllers, and services
2.Expertise on behavioral anomaly detection, abuse pattern detection (credential stuffing, BOLA/BFLA, scraping), and DDoS rate?limiting via platform policies and gateway integrations.
3. Map API controls to frameworks and regulations (e.g., ISO 27001, SOC 2, PCI DSS, HIPAA, GDPR).
4. Understanding of OWASP API Security Top 10, rate limiting, caching, idempotency, and back?pressure patterns.
5.Experience with OpenAPI/Swagger, schema governance, and contract testing; familiarity with GraphQL security.
6. Hands?on with Salt Security, 42Crunch, and Noname Security (deployment, policy creation, tuning, and integrations)
Responsibility –
1. Define the API security architecture and standards spanning discovery ? design review ? pre?prod testing ? runtime protection ? monitoring & response.
2. Establish reference architectures integrating Salt / 42Crunch / Noname with gateways (Apigee, Kong, Azure API Management), CI/CD, SIEM/SOAR, and IAM.
3. Establish and enforce API security standards, secure design patterns, and governance controls aligned with Zero?Trust and OWASP API Security principles.
4. Define API authentication and authorization models (OAuth2, OIDC, JWT, mTLS, token scoping, audience restrictions).
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
