About the Role
We are looking for a Lead AI/ML Engineer to define, build, and scale our AI platform. This is a hands-on leadership role: you’ll set technical direction, own the ML roadmap, and mentor a small team (Mid-level ML Engineer + DevOps/MLOps Engineer). You’ll collaborate directly with the executive team and receive strategic guidance ~15–20 hours/week from an external AI advisor.
Your mission: deliver production-ready ML/NLP solutions that are well-documented, compliant, and ready to hand off to the company’s Execution/Infra teams for deployment and long-term monitoring.
Key Responsibilities
- Technical Leadership: Define the AI/ML strategy and roadmap under the acceleration blueprint provided by the Advisor. Architect model training, evaluation, and deployment-ready pipelines using MLflow, Airflow/Prefect, and Feast. Establish engineering standards for quality, reproducibility, and compliance.
- Model Development: Build and package ML/NLP models using PyTorch, Hugging Face Transformers, and scikit-learn. Ensure all models are containerized and API-ready (FastAPI, gRPC) for easy deployment. Deliver explainable, auditable models with tools such as SHAP, LIME, and golden-set regression.
- Data & Compliance: Oversee data pipelines for structured and unstructured data (FHIR/HL7, payer systems). Implement data validation using Great Expectations and enforce HIPAA/PHI safeguards. Document data flows and compliance checks for downstream auditability.
- Team Mentorship & Collaboration: Guide and mentor the Mid-Level ML Engineer in building reproducible pipelines and models. Collaborate with the DevOps/MLOps Engineer to ensure artifacts are packaged with IaC, CI/CD, and monitoring hooks. Partner with product/operations to ensure solutions align with workflow needs.
- Handoff to Execution/Infra: Deliver ready-to-use code, containers, and documentation that the Execution/Infra teams can deploy and monitor. Ensure a smooth transition with clear runbooks and versioned artifacts. Maintain accountability for build quality, but not ongoing production operations.
Qualifications
- Education/Experience: Master’s or Ph.D. in Computer Science, Data Science, or a related field (or equivalent experience). 6–10+ years of experience in AI/ML, with 2+ years in a lead/principal role. Prior experience in healthcare, fintech, or other regulated industries is preferred.
- Technical Skills: Proficient in ML/NLP (PyTorch, Hugging Face, scikit-learn), MLOps (MLflow, Feast, Airflow/Prefect, Great Expectations), and Deployment Packaging (Docker, Kubernetes, KServe/Seldon, REST/gRPC APIs). Familiarity with Cloud/Infra Awareness (AWS/GCP/Azure HIPAA-eligible services; Terraform/CloudFormation) and Compliance (HIPAA/PHI safeguards, de-identification, audit requirements).
- Leadership Skills: Proven ability to take models from concept to deployable artifacts. Experience mentoring engineers and setting best practices. Strong communication skills across technical and business stakeholders. Thrives in a fast-moving environment with autonomy and accountability.
