Gen AI Senior Engineer

3 days ago

Hyderabad, Telangana, India Tata Consultancy Services Full time ₹ 8,00,000 - ₹ 24,00,000 per year

Were look for a senior Gen AI engineer to take LLM/Agent/RAG/Text-to-SQL prototypes to secure, observable, cost-efficient production. Youll own the full lifecycledata ingestion, retrieval quality, agent/tooling, API and UI surfaces, CI/CD, monitoring, and post-launch iteration.

What Youll Do

  • Convert research POCs into production-grade services with SLAs, on-call readiness, and guardrails.
  • Design and ship agentic workflows (tool use, function calling, orchestration graphs, retries, fallbacks).
  • Build robust RAG pipelines: ingestion chunking indexing retrieval re-ranking grounded generation.
  • Implement evaluation & LLMOps: golden sets, regression tests, CI eval gates, telemetry dashboards, A/B and canarying.
  • Build and document FastAPI backends (async, OpenAPI, pagination, rate limits, idempotency, background jobs).
  • Add observability (OpenTelemetry, logs/metrics/traces, cost & token spend) and performance/cost tuning.

Minimum Qualifications

  • 5+ years building production web apps/services; 2+ years with LLMs/RAG/agents.
  • Proven end-to-end ownership: shipped at least one LLM/Agent system to real users.
  • Strong Python , FastAPI, async patterns; TypeScript with
  • Solid RAG fundamentals (indexing, embeddings, hybrid search, re-ranking, evals) and Agent patterns (tooling, retries, timeouts, circuit breakers).
  • Experience with vector databases (pgvector/Milvus/Weaviate) and Postgres.
  • CI/CD (GitHub Actions), Containers/K8s, IaC (Terraform), and cloud (AWS/Azure/GCP).
  • Testing discipline: unit/integration/e2e, load tests, contract tests; strong debugging & profiling.
  • Clear communication, product sense, and trade-off thinking (latency/cost/quality/safety).

Preferred Qualifications

  • LangGraph/CrewAI/AutoGen (or equivalent) for multi-agent orchestration; tool calling best practices.
  • Retrieval evaluation with RAGAS/TruLens, custom golden sets, offline sims/trace replay.
  • Prompt/version control, dataset/versioning, and experiment tracking; feature flags & canary deploys.
  • Frontend polish: accessibility, performance budgets, error/retry UX, React Query/Zustand.
  • Security reviews for LLMs/agents (prompt injection, data exfiltration, DLP, policy enforcement).

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