Senior Machine Learning Engineer

2 weeks ago

Mumbai, Maharashtra, India Nova Full time
About Nova:

Nova is building AI teammates that help businesses protect revenue, prevent invisible losses, and free teams from repetitive work. We focus on high-impact areas like reconciliations, contract reviews, and compliance tracking; turning manual tasks into ROI-driven automation that empowers people to do their best work.

About the role:

We're looking for an experienced Machine Learning Engineer (MLE) to lead our ML efforts end-to-end. From architecting agentic AI workflows to optimizing model pipelines in production. You'll own how our AI backbone scales, collaborate with backend and product teams, and ensure our agents are not just powerful but also reliable, cost-efficient, and explainable.

This is a hands-on role: you'll build, but also guide, mentor, and influence the ML direction at Nova.

What you will work on:

- Architect and scale ML pipelines powering Nova's AI teammates.

- Implement multi-LLM, multi-agent orchestration (e.g., LangGraph or equivalents) with routing, chaining, retries, tool-use, guardrails, and persisted state.

- Design and optimize RAG pipelines (hierarchical chunking, embeddings, hybrid search, re-ranking).

- Build and maintain connectors (via MCP and other frameworks) so agents can reliably discover and integrate with external systems

- Adapt and fine-tune models for Finance, Legal, and Tax workflows (e.g., extraction, summarization, anomaly detection).

- Deploy and monitor models in production using APIs, tracing, metrics, and balancing cost with performance.

- Create a culture where ML is built for production from day one — fast experiments that are measurable, reproducible, and ready to scale.

What we are looking for:

- 4–5 years as an ML Engineer / Applied Scientist working in production environments.

- Strong Python (FastAPI/Flask), PyTorch/TensorFlow, and data pipeline expertise.

- Deep knowledge of LLMs, RAG systems, embeddings, and vector DBs (PGVector/Postgres preferred).

- Experience working with multiple LLM providers (OpenAI, Anthropic, Google, open-source models) and balancing cost, latency, and quality.

- Comfortable with Docker, Kubernetes, CI/CD, and deploying to AWS/GCP.

- Bonus: experience with LangGraph/LangChain, vLLM/Ray, LLM evaluation frameworks, or event-driven architectures.

What you will get:

- A chance to build from 0 to 1 the AI backbone of a product targeting a $100M+ ARR opportunity.
- End-to-end ownership and visibility, your code will run in production, not in notebooks.
- A front-row seat to shape how agentic AI gets applied to real-world, high-stakes problems.

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