Senior FullStack Engineer
Role Summary:
You spend equal parts of your time sitting with clients to understand what they actually need, designing the system, and translating ambiguity into business understanding, architecture, and delivery plans, while also staying very close to the code to lead through what you produce, not just through what you specify.
This role exists because most senior architects we meet have stopped writing code, and most senior engineers we meet have never sat across from a client. We need someone who does both, and prefers it that way.
AI is woven through almost everything we deliver. You will work with LLMs, RAG, and agentic systems as well as broader ML concepts and models, bringing either strong conceptual grounding or hands-on experience in ML, and you are a senior engineer who treats AI as one more capability in a stack that also includes Python, TypeScript, FastAPI, React, Postgres, and whatever else the problem needs.
The split is roughly 30% solutioning and client engagement, 70% hands-on engineering. The exact balance flexes with the project.
Why Join Us
JoulestoWatts is a 5,500+ person enterprise services company serving 300+ Global Capability Centers across India. We build, staff, and operate technology and operations capability for some of the largest global enterprises in the country.
We are investing seriously in AI as a differentiator: shipping internal AI products, embedding AI into client engagements, and developing platform capabilities that go beyond traditional services. This role sits at the center of that bet.
You will work directly with the leadership team. You will see clients across multiple industries. You will see systems go into production with real users in months, not quarters. And you will do it as part of a team that values shipping over status.
What You Will Do:
Hands-On Engineering
Own the architecturally critical pieces yourself. Production code, not pseudocode and whiteboards.
Design and implement full-stack systems end-to-end: backend services, data models, frontend interfaces, integrations, deployment.
Use AI coding tools (Claude Code, Cursor, Copilot) fluently to move fast. You own everything they produce.
Bring AI components into solutions where they fit: LLM integrations, RAG pipelines, agentic workflows, evaluations. You know when AI is the answer and when it is not.
Stay current on the modern stack and bring new patterns into the team's work as they prove out.
Experience building and deploying applications in cloud environments, with a solid understanding of cloud-native architecture, scalability, and operational considerations.
Technical Leadership
Lead delivery teams as a player-coach. Code reviews, pair programming, unblocking, mentoring.
Set the technical bar through what you produce, not through process documents.
Translate solution architecture into assignable streams. Own the integration points and the hard parts; delegate the rest with clear interfaces.
Grow the engineers you work with. People should be visibly stronger six months after working with you.
Client Solutioning
Sit with clients (CTOs, Heads of Engineering, business sponsors) to understand the as-is, probe for the real problem, and translate ambiguity into clear business understanding.
Translate that understanding into architecture, solution proposals, and delivery plans that survive contact with reality.
Support pre-sales pursuits with technical scoping, architecture sketches, and feasibility assessments. You do not own the commercial close, but you make the technical case.
Run discovery sessions and workshops where you ask better questions than the client expected. The bar is to leave the room with a sharper problem statement than you walked in with.
Technical Skills
This is the stack we expect you to be fluent in. Not every project uses every item, but a senior candidate will have hands-on production experience across most of these.
Backend: Python (FastAPI, Pydantic, etc.), Node.js / TypeScript (Express, NestJS), REST API design, OAuth2 / JWT / RBAC.
Frontend: React, Next.js, TypeScript end-to-end, Tailwind and shadcn/ui, modern data fetching and state.
Databases and data: Postgres (schema design, query optimization, migrations), MySQL, MongoDB, Redis, vector stores (pgvector, Pinecone, Weaviate, FAISS), basic ETL and pipeline patterns.
Cloud and infrastructure: AWS (ECS, Lambda, S3, RDS, Bedrock); GCP or Azure equivalents acceptable. Docker, CI/CD (GitHub Actions, GitLab CI), observability and monitoring, cost-aware deployment.
AI and ML: LLM APIs (OpenAI, Claude, Gemini), end-to-end RAG (chunking, embeddings, retrieval, re-ranking, evaluation), agent frameworks (LangChain, LangGraph), MCP and tool calling, knowledge graphs (Neo4j or equivalent), evaluation and observability (RAGAS, DeepEval, LangSmith, Langfuse).
Architectural paradigms: Microservices and modular monoliths, event-driven patterns, async and queue-based workflows, multi-tenant design.
Developer tooling: Git and trunk-based workflows, AI-assisted coding (Claude Code, Cursor, Copilot) at production fluency, pragmatic testing, technical writing (architecture docs, ADRs, proposals).
Engineering Experience
We are looking for someone with 8+ years of full-stack engineering experience and a consistent shipping record. Beyond the stack, what matters:
You have personally delivered production systems end-to-end, more than once. You can walk through architecture decisions, what broke, and what you would do differently, without notes.
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, not just the one that ships next week. You also know when to stop designing and start building.
You are comfortable owning ambiguity. Vague problem statements are an opportunity, not a blocker.
You read code well, write it cleanly, and review it generously. Your default is to make the next person's job easier.
You have shipped in environments without a safety net (early-stage products, greenfield client engagements, internal zero-to-one builds) and you know what that costs.
AI Building 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 engineering.
You have shipped real AI products with users on the other end. You can name the systems, the stack, the failure modes, and what you fixed.
You understand the difference between a demo 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 said no to AI-where-AI-does-not-fit and can explain why.
You are comfortable with the pace of the field. You read, ship, iterate. You do not require a stable spec to make progress.
You can explain LLM behavior to a non-technical client without dumbing it down or overselling it.
Client and Communication Skills
You have worked directly with external clients, not only internal stakeholders. You are comfortable in senior client conversations: CTOs, VPs, business sponsors.
You can probe a vague client request and surface the actual problem. The skill we are testing is whether you ask better questions, not just give better answers.
You write well. You can produce a one-page proposal, an architecture document, or a clean Slack update with equal ease.
You present technical work to non-technical audiences without losing them.
Leadership
You have led engineers through delivery, whether one direct report or a small team. You know the difference between leading and managing, and prefer the former.
You have mentored other engineers 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 code review.
You are comfortable being the most senior person in the room and also comfortable not being.
Nice to Have:
Founder or early-stage startup experience. We move fast and prefer people who have lived in that mode.
Open-source contributions, technical writing, or a public body of work that shows how you think.
Domain experience in enterprise services, GCC operations, or recruitment-tech.
Specific exposure to production agentic systems.
Pre-sales or solution-selling experience in a services or platform context.
What This Role Is Not
Not a pure architect role. If your last two years have been slide decks and review boards, this is not the right fit.
Not a pure IC role. We need someone who can lead delivery and grow other engineers.
Not a research role. We ship production AI, not papers.
Not a pre-sales role. You support the sales motion; you do not own it.
Not a delivery management role. You should know what your team is shipping because you are reviewing the pull requests.
How We Will Evaluate You
A working code sample or GitHub history we can read. We care about what you have actually delivered.
A technical conversation walking through one of your past projects in depth. We will ask about trade-offs, 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 not graded on the answer; it is graded on the questions you ask.
A short paired session, live, on a small AI-flavored task. We want to see how you work, not just what you can describe.
A reference conversation with someone you have led or mentored.
Responsibilities
Senior FullStack Engineer
Role Summary:
You spend equal parts of your time sitting with clients to understand what they actually need, designing the system, and translating ambiguity into business understanding, architecture, and delivery plans, while also staying very close to the code to lead through what you produce, not just through what you specify.
This role exists because most senior architects we meet have stopped writing code, and most senior engineers we meet have never sat across from a client. We need someone who does both, and prefers it that way.
AI is woven through almost everything we deliver. You will work with LLMs, RAG, and agentic systems as well as broader ML concepts and models, bringing either strong conceptual grounding or hands-on experience in ML, and you are a senior engineer who treats AI as one more capability in a stack that also includes Python, TypeScript, FastAPI, React, Postgres, and whatever else the problem needs.
The split is roughly 30% solutioning and client engagement, 70% hands-on engineering. The exact balance flexes with the project.
Why Join Us
JoulestoWatts is a 5,500+ person enterprise services company serving 300+ Global Capability Centers across India. We build, staff, and operate technology and operations capability for some of the largest global enterprises in the country.
We are investing seriously in AI as a differentiator: shipping internal AI products, embedding AI into client engagements, and developing platform capabilities that go beyond traditional services. This role sits at the center of that bet.
You will work directly with the leadership team. You will see clients across multiple industries. You will see systems go into production with real users in months, not quarters. And you will do it as part of a team that values shipping over status.
What You Will Do:
Hands-On Engineering
Own the architecturally critical pieces yourself. Production code, not pseudocode and whiteboards.
Design and implement full-stack systems end-to-end: backend services, data models, frontend interfaces, integrations, deployment.
Use AI coding tools (Claude Code, Cursor, Copilot) fluently to move fast. You own everything they produce.
Bring AI components into solutions where they fit: LLM integrations, RAG pipelines, agentic workflows, evaluations. You know when AI is the answer and when it is not.
Stay current on the modern stack and bring new patterns into the team's work as they prove out.
Experience building and deploying applications in cloud environments, with a solid understanding of cloud-native architecture, scalability, and operational considerations.
Technical Leadership
Lead delivery teams as a player-coach. Code reviews, pair programming, unblocking, mentoring.
Set the technical bar through what you produce, not through process documents.
Translate solution architecture into assignable streams. Own the integration points and the hard parts; delegate the rest with clear interfaces.
Grow the engineers you work with. People should be visibly stronger six months after working with you.
Client Solutioning
Sit with clients (CTOs, Heads of Engineering, business sponsors) to understand the as-is, probe for the real problem, and translate ambiguity into clear business understanding.
Translate that understanding into architecture, solution proposals, and delivery plans that survive contact with reality.
Support pre-sales pursuits with technical scoping, architecture sketches, and feasibility assessments. You do not own the commercial close, but you make the technical case.
Run discovery sessions and workshops where you ask better questions than the client expected. The bar is to leave the room with a sharper problem statement than you walked in with.
Technical Skills
This is the stack we expect you to be fluent in. Not every project uses every item, but a senior candidate will have hands-on production experience across most of these.
Backend: Python (FastAPI, Pydantic, etc.), Node.js / TypeScript (Express, NestJS), REST API design, OAuth2 / JWT / RBAC.
Frontend: React, Next.js, TypeScript end-to-end, Tailwind and shadcn/ui, modern data fetching and state.
Databases and data: Postgres (schema design, query optimization, migrations), MySQL, MongoDB, Redis, vector stores (pgvector, Pinecone, Weaviate, FAISS), basic ETL and pipeline patterns.
Cloud and infrastructure: AWS (ECS, Lambda, S3, RDS, Bedrock); GCP or Azure equivalents acceptable. Docker, CI/CD (GitHub Actions, GitLab CI), observability and monitoring, cost-aware deployment.
AI and ML: LLM APIs (OpenAI, Claude, Gemini), end-to-end RAG (chunking, embeddings, retrieval, re-ranking, evaluation), agent frameworks (LangChain, LangGraph), MCP and tool calling, knowledge graphs (Neo4j or equivalent), evaluation and observability (RAGAS, DeepEval, LangSmith, Langfuse).
Architectural paradigms: Microservices and modular monoliths, event-driven patterns, async and queue-based workflows, multi-tenant design.
Developer tooling: Git and trunk-based workflows, AI-assisted coding (Claude Code, Cursor, Copilot) at production fluency, pragmatic testing, technical writing (architecture docs, ADRs, proposals).
Engineering Experience
We are looking for someone with 8+ years of full-stack engineering experience and a consistent shipping record. Beyond the stack, what matters:
You have personally delivered production systems end-to-end, more than once. You can walk through architecture decisions, what broke, and what you would do differently, without notes.
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, not just the one that ships next week. You also know when to stop designing and start building.
You are comfortable owning ambiguity. Vague problem statements are an opportunity, not a blocker.
You read code well, write it cleanly, and review it generously. Your default is to make the next person's job easier.
You have shipped in environments without a safety net (early-stage products, greenfield client engagements, internal zero-to-one builds) and you know what that costs.
AI Building 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 engineering.
You have shipped real AI products with users on the other end. You can name the systems, the stack, the failure modes, and what you fixed.
You understand the difference between a demo 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 said no to AI-where-AI-does-not-fit and can explain why.
You are comfortable with the pace of the field. You read, ship, iterate. You do not require a stable spec to make progress.
You can explain LLM behavior to a non-technical client without dumbing it down or overselling it.
Client and Communication Skills
You have worked directly with external clients, not only internal stakeholders. You are comfortable in senior client conversations: CTOs, VPs, business sponsors.
You can probe a vague client request and surface the actual problem. The skill we are testing is whether you ask better questions, not just give better answers.
You write well. You can produce a one-page proposal, an architecture document, or a clean Slack update with equal ease.
You present technical work to non-technical audiences without losing them.
Leadership
You have led engineers through delivery, whether one direct report or a small team. You know the difference between leading and managing, and prefer the former.
You have mentored other engineers 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 code review.
You are comfortable being the most senior person in the room and also comfortable not being.
Nice to Have:
Founder or early-stage startup experience. We move fast and prefer people who have lived in that mode.
Open-source contributions, technical writing, or a public body of work that shows how you think.
Domain experience in enterprise services, GCC operations, or recruitment-tech.
Specific exposure to production agentic systems.
Pre-sales or solution-selling experience in a services or platform context.
What This Role Is Not
Not a pure architect role. If your last two years have been slide decks and review boards, this is not the right fit.
Not a pure IC role. We need someone who can lead delivery and grow other engineers.
Not a research role. We ship production AI, not papers.
Not a pre-sales role. You support the sales motion; you do not own it.
Not a delivery management role. You should know what your team is shipping because you are reviewing the pull requests.
How We Will Evaluate You
A working code sample or GitHub history we can read. We care about what you have actually delivered.
A technical conversation walking through one of your past projects in depth. We will ask about trade-offs, 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 not graded on the answer; it is graded on the questions you ask.
A short paired session, live, on a small AI-flavored task. We want to see how you work, not just what you can describe.
A reference conversation with someone you have led or mentored.
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
