Machine Learning Engineer

we are looking for a Machine Learning Engineer.What You'll Do:

• Design and ship the ML backbone of Gini AI Workers - routing, tool selection, reasoning, memory, evaluation.
• Build evaluation and feedback loops - offline evals, online A/B, regression harnesses, human-in-the-loop labeling pipelines.
• Optimize cost and latency across the agent stack: prompt engineering, model routing (frontier ↔ small ↔ fine-tuned), caching, speculative decoding, distillation.
• Fine-tune and/or RAG-tune models for vertical enterprise tasks (invoice extraction, PO matching, ticket triage, forecasting).
• Own the ML infra - training pipelines, experiment tracking, model registry, deployment, monitoring, drift detection.
• Partner with backend product to turn research into shipped features on a weekly cadence.Requirements:
• 4 years of ML engineering in production (not just research or notebooks).
• Hands-on LLM experience in 2025-2026: agentic systems, tool-use, function-calling, RAG, structured output, eval design.
• Strong Python. Comfortable with PyTorch/JAX and one serving stack (vLLM, TGI, TensorRT-LLM, SageMaker, or similar).
• You've built an eval pipeline that actually caught a regression in prod.
• You read the papers and know which ones to ignore.Nice to Have:
• Experience with MCP, LangGraph, DSPy, or custom agent frameworks.
• Fine-tuning (LoRA/QLoRA, DPO/ORPO, RLAIF) on open-weight models (Llama, Qwen, Mistral, DeepSeek).
• Vector DBs (pgvector, Pinecone, Weaviate, Qdrant), reranking, hybrd retreval.
• Prior work on multi-agent systems or enterprise copilots.This position is open to all candidates.