AI Platform Engineer

Job Description :

• 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