Lead ML/AI Engineer
Core
Build production infrastructure for real-time ML decisioning and generative AI applications in healthcare, connecting models to measurable actions and outcomes.
Role type
Senior individual contributor Lead ML/AI Engineer
Builds
Real-time decision platform, AWS-native infrastructure, Databricks lakehouse, streaming pipelines, and generative AI applications
Domain
Healthcare / Machine Learning / MLOps / Generative AI
Deliverable
production ML models
Required skills
AWS services, Databricks, PySpark, Delta Lake, MLflow, Python, SQL, Terraform, CDK, CloudFormation, Kinesis, Kafka, CI/CD, model monitoring, cost optimization, technical leadership, architecture design
Preferred skills
scikit-learn, XGBoost, PyTorch, dbt, uplift modeling, bandit algorithms, containerized model serving, Amazon Bedrock, agentic frameworks, LLM evaluation
Technologies
AWS, Databricks, PySpark, Delta Lake, MLflow, Terraform, CDK, CloudFormation, Kinesis, Kafka, Python, SQL, scikit-learn, XGBoost, PyTorch, dbt, Amazon Bedrock
Responsibilities
Own decisioning and feedback layer connecting model scores to actions; Lead engineering implementation of production savings calculations; Design event-driven, real-time integrations between model outputs and operational systems; Define and maintain integration contracts with partner teams; Establish cost-to-serve standards for compute, streaming, and inference; Set production standards for ML models including CI/CD and drift monitoring; Build and maintain model promotion gates; Own target architecture and technical roadmap for AWS-native infrastructure and Databricks lakehouse; Define technical direction for generative AI applications; Collaborate with data scientists, medical economics, and engineering teams; Establish engineering standards for code reviews and testing; Lead architecture reviews and mentor engineers; Represent technical direction to stakeholders
Seniority
Senior, hands-on IC with significant technical ownership
