Description :

Backend Engineer - AI/ML Platform

Experience : 6 - 10 years (Senior SDE / Staff-ready Senior)

Engagement : Contract (full time)

Location : Mumbai or Bangalore (multi-location team; occasional travel)

A hands-on backend engineering role building the core AI/ML-backed systems that power BharatIQs consumer experiences at scale. This role is not learn ML on the job.

You must already be effective building and shipping ML-adjacent backend systems (RAG/retrieval, embeddings, ranking, evaluation hooks, feature pipelines) and making pragmatic tradeoffs across quality, latency, and cost.

We cannot upskill on ML fundamentals in this engagement; candidates must demonstrate prior delivery of ML-backed backend systems in production.

The Engineer will :

- Build and operate core backend services for AI product runtime : orchestration, state/session, policy enforcement, tools/services integration

- Implement retrieval + memory primitives end-to-end : chunking, embeddings generation, indexing, vector search, re-ranking, caching, freshness and deletion semantics

- Productionize ML workflows and interfaces : feature/metadata services, online/offline parity, model integration contracts, and evaluation instrumentation

- Drive performance and cost optimization (P50/P95 latency, throughput, cache hit rates, token/call cost, infra efficiency) with strong SLO ownership

- Add observability-by-default : tracing, structured logs, metrics, guardrail signals, failure taxonomy, and reliable fallback paths

- Collaborate with applied ML on model routing, prompt/tool schemas, evaluation datasets, and release safety gates

What were looking for (must-have) :

- 6 - 10 years building backend systems in production, including at least 2 - 3 years on ML/AI-backed products (search, recommendations, ranking, RAG, or assistants)

- Practical ML chops : able to reason about embeddings, vector similarity, re-ranking, retrieval quality, evaluation metrics (precision/recall, nDCG, MRR), and data drift - without needing training

- Experience implementing or operating RAG pipelines (document ingestion, chunking strategies, indexing, query understanding, hybrid retrieval, re-rankers)

- Strong distributed systems fundamentals : API design, idempotency, concurrency, rate limiting, retries, circuit breakers, and multi-tenant reliability

- Comfort with common ML/AI platform components : feature stores/metadata, streaming/batch pipelines, offline evaluation jobs, A/B measurement hooks

- Ability to ship end-to-end independently : design - build - deploy - operate in a fast-moving environment

Bonus (nice to have) :

- Agentic runtime / tool-calling patterns, function calling schemas, structured outputs, safety/guardrails in production

- Prior work with FAISS / Milvus / Pinecone / Elasticsearch hybrid retrieval, and model serving stacks

- Kubernetes + observability stack depth (OpenTelemetry, Prometheus/Grafana, distributed tracing), plus privacy controls for user data.