Experience : 3 - 15 Yrs

Location : Bengaluru

Designation : Senior Engineer / Lead Engineer / Engineering Manager - AI

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 explicit 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 solid 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 key 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.

📌 Generative Ai Engineer Bengaluru
🏢 e-Hireo
📍 Bengaluru