Senior Machine Learning Engineer

Role Context

Nenu AI builds applied AI systems that must survive real users real data and real constraints. This role is not research theater. It is production ownership. You will design build deploy and operate ML systems that directly shape the product. Expect ambiguity. Expect velocity. Expect accountability.

• End-to-end ML lifecycle across real products. Data ingestion feature design model selection training deployment monitoring iteration. No handoffs.
• Production-grade ML systems built with PyTorch or Tensor Flow with attention to latency reliability cost and failure modes.
• Applied GenAI and LLM work where it creates measurable value. Fine-tuning RAG prompt orchestration evaluation and guardrails. No hype-first work.
• MLOps foundations. Model versioning CI,CD automated testing deployment pipelines serving layers monitoring and A,B experimentation.
• Tight partnership with product engineering and data. Translate fuzzy business problems into tractable ML solutions and quantify impact.
• Technical leadership. Code reviews model reviews mentoring and raising the bar for ML engineering discipline.
• Incident ownership. Debug production failures data drift performance regressions and bias issues calmly and decisively.

7+ years of hands-on ML engineering with clear senior-level ownership of