Lead the technical architecture and engineering strategy for integrating sophisticated AI into high-stakes Healthcare Information Systems (HIS).
Architect robust MLOps pipelines and cloud infrastructure required to move models from experimental notebooks into mission-critical clinical environments.
Design and implement end-to-end ML lifecycles focusing on automated CI/CD pipelines, model versioning and reproducible experimentation.
Architect high-performance serving layers for both LLMs and classical models, ensuring low-latency and high-availability in a secure healthcare cloud environment.
Design robust data pipelines to process healthcare-specific formats into high-quality features for real-time and batch inference.

Bachelors Degree or Higher in Computer Science, Software Engineering, or a related technical field.
10+ years of experience in software engineering, with at least 6 years dedicated to deploying and maintaining large-scale ML systems in production.
Expert-level experience with Cloud Providers (AWS/GCP/Azure) and orchestration tools (Kubernetes, Kubeflow, or Airflow).
Expert-level Python and Java/Go (or similar).
Strong experience with Spark, Snowflake/Databricks, and building scalable feature stores.
Hands-on experience deploying Generative AI (LLMs) and Agentic frameworks (LangChain/LangGraph).

Principal ML Engineer

Key skills