Senior Full Stack AI Engineer
Bengaluru, IndiaHybridFull-timeMid · 5-8 years
About this role
We’re hiring a Full Stack AI Engineer to build AI-native products end to end: applications, agents, RAG/GraphRAG, NL2SQL, evals, and observability. You’ll turn LLM capabilities into reliable, user-facing features that are measurable, debuggable, and safe in production.
What you’ll do
- Build and own full-stack AI features across frontend, backend, and data layers for web applications.
- Design agentic workflows (single- and multi-agent / A2A) that can plan, route, call tools, and coordinate to complete complex tasks.
- Implement and refine RAG pipelines, including retrieval strategies, chunking, embeddings, reranking, and hybrid search across multiple data sources.
- Design and operate GraphRAG-style retrieval on top of knowledge graphs to support multi-hop reasoning and relationship-heavy use cases.
- Build NL2SQL / NL2DB capabilities that convert natural language into safe, validated queries against SQL databases, warehouses, or analytics systems.
- Define and manage tool interfaces and MCP-style capability layers so agents can call internal APIs, SaaS tools, and data services with proper contracts and permissions.
- Create evaluation pipelines for prompts, agents, RAG, GraphRAG, NL2SQL, and tool use, including regression tests, LLM-as-judge scoring, and human review loops.
- Instrument AI systems with traces, logs, metrics, and structured events so you can debug failures, track versions, and understand behavior across the entire request path.
- Build dashboards and alerts to monitor quality, latency, cost, and safety signals for AI features in production.
- Collaborate with product, design, data, and platform teams to move from prototype to production while adding guardrails, fallbacks, and human-in-the-loop flows where needed.
- Continuously experiment with new models, prompting techniques, and architectures, then distill what works into reusable patterns and libraries for the team.
Required qualifications
- Hands-on experience shipping LLM-based features (agents, RAG, tool calling, or NL2SQL) into production.
- Strong full-stack engineering experience with modern web stacks (e.g., TypeScript/React/Next.js plus Python/Node.js).
- Solid backend fundamentals: REST/GraphQL APIs, relational databases, caching, and cloud deployment (AWS/GCP/Azure with Docker/CI).
- Experience designing, measuring, and improving AI behavior using evals, metrics, and real user feedback.
- Ability to work closely with product teams, own projects end to end, and make pragmatic tradeoffs between quality, speed, and cost.
Tech Stack
| Area | Example tools |
| Frontend | React, Next.js, TypeScript, Tailwind |
| Backend | Python, FastAPI, Node.js, PostgreSQL, Redis |
| AI orchestration | LangChain, LangGraph, Semantic Kernel, custom agent frameworks |
| Retrieval | Pinecone, Weaviate, FAISS, Elasticsearch, hybrid search |
| Graph / GraphRAG | Neo4j, graph stores, entity linking, knowledge graph pipelines |
| Evals | LangSmith, DeepEval, custom benchmark suites, human review workflows |
| Observability | Langfuse, Arize, W&B, OpenTelemetry, custom dashboards |
| Infra | AWS, Docker, Kubernetes, GitHub Actions |