AI​/ML Engineer - GenAI - Data

Key Responsibilities

• Challenge unclear or technically weak requirements with practical alternatives.
• Translate clinical and product requirements into clear, testable technical specifications.
• Proactively identify risks and failure modes before implementation.

• Challenge unclear or technically weak requirements with practical alternatives.
• Translate clinical and product requirements into clear, testable technical specifications.
• Proactively identify risks and failure modes before implementation.

• Design and build end-to-end RAG pipelines (ingestion, chunking, embeddings, vector stores, retrieval, generation).
• Integrate and evaluate LLMs (OpenAI, Claude, Gemini) with a focus on response quality, hallucination mitigation, and healthcare safety.
• Develop AI services using Lang Chain/Lang Graph, Python, Flask, and REST APIs.
• Build prompt engineering and LLM evaluation frameworks covering relevance, accuracy, safety, and tone.
• Implement and optimize vector databases (FAISS, Pinecone, Weaviate) and embedding pipelines.

• Develop ML models and health signals from physiological data (CGM, HRV, sleep, activity, heart rate).
• Engineer meaningful features for time-series health data and evaluate model confidence, accuracy, and edge cases.
• Apply explainable and clinically defensible ML approaches, choosing the simplest effective model.

• Build, debug, and maintain data pipelines in Python and C#.
• Manage end-to-end health data flow from connected devices through ingestion, transformation, storage, and AI consumption.
• Resolve data quality issues and work with structured/time-series data, including normalization, windowing, gap handling, sensor dropouts, and timezone-aware aggregation.

• Build clinically safe AI systems that detect and surface incorrect or uncertain outputs.
• Ensure HIPAA-compliant data handling and healthcare best practices.
• Collaborate closely with clinicians to develop production-ready AI features.
• Own features end-to-end, from requirements and deployment to monitoring, evaluation, and production debugging.
• Non-negotiable
• Demonstrated reasoning ability the capacity to look at an ambiguous problem, break it down, identify what is
• missing, and propose a path forward. This is the primary screen.
• Genuine hands-on GenAI experience — building RAG pipelines, LLM-integrated features, prompt engineering
• with evaluation. Real systems, real failure modes, real fixes. Not course projects.
• Python proficiency — clean, testable, production-quality code. Flask or equivalent API framework experience.
• C# / .NET literacy — you can read, debug, and contribute to data pipelines written in C#. You do not need to be
• a C# architect, but data engineering is a daily reality in this role and you engage with it directly.
• Working knowledge of LLM integration: prompt engineering, evaluation, hallucination risk, and safety
• constraints. You have a mental model of when models fail and why.
• Enough ML grounding to think clearly about signals from health data — HRV, CGM, sleep, activity — without
• needing to be a deep learning researcher.
• Vector database experience — you understand retrieval quality trade-offs, not just the API calls.
• Ownership instinct — the kind of person who is bothered by a bug in production even when it isn't technically
• assigned to them.
• Curiosity that is visible — in side projects, questions you ask, papers you've read, things you've broken on
• purpose to understand them.
• Advantageous:
• Strong advantage
• Experience building ML-based health signals from physiological time-series data — HRV, sleep stages, CGM,
• activity — and a clear understanding of what makes those signals reliable or unreliable.
• Experience in healthcare or health-tech AI — clinical accuracy requirements, safe AI design, and the stakes of a
• wrong answer in a medical context.
• Lang Chain or Lang Graph experience — particularly building multi-step pipelines with memory, tool use, and
• evaluation.
• Cloud platform experience (AWS, GCP, or Azure) — deploying and monitoring AI services in production.
• GraphQL APIs and Next.js — for collaboration with the frontend team.
• Familiarity with HIPAA-compliant data pipelines and healthcare data handling standards.
• Experience building AI evaluation frameworks — not just shipping models, but measuring whether they are
• working.