Senior, Data Scientist - MLE

Design, build, and productionize machine learning solutions end-to-end with strong ML engineering practices and agentic AI systems. You will partner with product and engineering stakeholders to deliver measurable impact via scalable models, intelligent workflows, and evaluation frameworks.

• Build production ML systems with training/inference pipelines, serving patterns, CI/CD for ML, and observability
• Develop agentic AI solutions using LLM-based agents and orchestration patterns
• Build RAG/knowledge systems with indexing, chunking, embeddings, and reranking
• Create evaluation and experimentation frameworks for ML and LLM/agent systems (golden sets, human-in-the-loop, A/B tests, guardrail metrics)
• Collaborate with data engineering and platform teams on data quality, lineage, governance, and scalable infrastructure

Build production ML systems with robust training/inference pipelines, model serving patterns, ML CI/CD, and observability for drift/performance/cost. Implement agentic AI using LLM-based agents (tool/function calling, orchestration, retrieval, memory) and RAG pipelines (indexing, chunking, embeddings, reranking), supported by offline/online evaluation and deployment using Airflow/Dagster/Prefect plus FastAPI/BentoML/TorchServe on Docker/Kubernetes.

The ideal candidate is a Senior Data Scientist - MLE with 5+ years of production ML experience, strong Python skills, and proven ML engineering execution across pipelines, serving, CI/CD, and observability. They also have hands-on experience building agentic AI / LLM systems including tool/function calling and RAG workflows using orchestration frameworks such as LangGraph/LangChain, Semantic Kernel, and LlamaIndex.