Responsibilities

- Own end-to-end delivery of AI-powered products — from problem framing and architecture through implementation, deployment, and production operations
- Design, build, and ship full-stack systems: frontend, backend APIs, data layers, and AI/ML services — not just models or prompts
- Take a product from idea to production independently: design APIs, build UIs, wire data flows, integrate models, and operate the system
- Build and productionize LLM / GenAI features (RAG, agents, tool-calling, evaluation, guardrails) and integrate them into real user-facing applications
- Design scalable application and model-serving architectures that are reliable, observable, and cost-aware
- Own data pipelines, retrieval, embeddings, and evaluation loops so AI features stay accurate and measurable in production
- Work across the stack as needed — React/Next (or equivalent), Node/Python/Go services, databases, queues, cloud, and CI/CD — without waiting on a specialist for every layer
- Partner with product, design, and engineering to turn ambiguous problems into shipped features
- Review code, raise the bar on system design, and mentor engineers on both AI and full-stack practices
- Establish engineering standards for AI quality, evaluation, security, and release confidence
- Debug production issues across the full path: UI, API, infra, data, and model behavior

Requirements

- 15+ years of software engineering, with recent hands-on ownership of production systems
- Proven full-stack depth:



can design and implement frontend, backend, APIs, data stores, and cloud infrastructure — not limited to AI-only work
- Hands-on with AI-assisted development tools (Cursor, GitHub Copilot, and similar) as part of day-to-day engineering
- Hands-on experience shipping LLM / GenAI or ML systems in production (RAG, agents, fine-tuning, evaluation, or equivalent)
- Comfortable choosing and using the right stack for the problem (e.g. TypeScript/Python, React or similar, REST/GraphQL, SQL/NoSQL, queues, object storage)
- Experience with CI/CD, distributed systems, and production debugging
- Robust ownership: can take an ambiguous brief and deliver a working product end to end
- Clear communication and the ability to work with product, design, and other engineers without hand-holding

Qualifications

- Degree in Computer Science / Engineering or equivalent practical experience
- Hands-on engineer with a production-level coding mindset — writes, reviews, and ships code
- Track record of building scalable, reliable systems, not just prototypes or notebooks
- Good documentation and collaboration skills
- Bias toward owning the whole problem: product, architecture, implementation, and operations.

Perks:

- Day off on the 3rd Friday of every month (one long weekend each month)
- Monthly Wellness Reimbursement Program to promote health well-being
- Monthly Office Commutation Reimbursement Program
- Paid paternity and maternity leaves

📌 Staff AI Engineer (Chennai)
🏢 Forbes Advisor
📍 Chennai