Generative AI Engineer

Requirement: AI Platform Engineer ( Gen AI Platform)
Experience: 3 to 5 yrs
Location: Bangalore ( 5 Days Work From Office)
What you'll do — shared across both tracks
•Own work end-to-end — design, build, test, ship, monitor, iterate — with production ownership and on-call.
•Write clean, well-tested, maintainable code and clear design docs; apply SOLID and sound API
design.
•Treat reliability, latency, cost, and observability as first-class requirements, not afterthoughts.
•Build for a regulated environment: data privacy, access controls, auditability, safe handling of
customer data.
•Collaborate across product, data, risk, and platform to turn ambiguous problems into measurable
outcomes.
Track A — Software / Platform Engineer
•Build and operate model-serving, gateway, and orchestration infra (routing, caching, rate-limiting,
fallbacks) for LLM/ML workloads.
•Design data and RAG pipelines — ingestion, chunking, embedding jobs, vector/index stores — that
stay fresh and consistent.
•Build guardrails, evaluation harnesses, prompt/version management, and observability (tracing,
metrics, cost attribution); harden for scale and failure.
•We look for: production backend/distributed systems in a strong language (Go, Java, Python…); solid concurrency, APIs, databases, queues, and cloud infra; a reliability mindset. Deep ML theory not required.
Track B — ML / Applied-AI Engineer
•Improve retrieval and RAG quality — chunking, embeddings, re-ranking, grounding — measured
against real metrics.
•Build agent and prompt workflows; systematically evaluate models, prompts, and pipelines with
offline and online evals.
•Fine-tune, adapt, or distill models where it clearly beats prompting; partner with the platform track to productionize to the same reliability bar.
•We look for: production-quality Python and service ownership (not just notebooks); hands-on LLMs, embeddings/retrieval, and evaluation, plus one of fine-tuning, RAG, or agent frameworks; rigor with data and experiments.
Common bar & nice-to-haves
•Strong CS fundamentals (data structures, algorithms, system design) and a track record of shipping in production.
•Clear communication and a bias for reliability and correctness — especially important in fintech.
•Nice to have: fintech / lending / payments or other regulated domains; LLMOps / MLOps tooling,
vector DBs, eval frameworks; open-source or AI/ML side projects.

Work arrangement
No