Lead the technical architecture and engineering strategy for integrating sophisticated AI into high-stakes Healthcare Information Systems (HIS).
Architect the robust MLOps pipelines and cloud infrastructure required to move models from experimental notebooks into mission-critical clinical environments.
Design and implementation of end-to-end ML lifecycles, focusing on automated CI/CD pipelines, model versioning (MLflow/DVC), 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.
Build the underlying infrastructure for agent-based reasoning systems, ensuring these

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).
Deep proficiency in backend frameworks and system design patterns.
Strong experience with Spark, Snowflake/Databricks, and building scalable feature stores.
Hands-on experience deploying Generative AI (LLMs) and Agentic frameworks (LangChain/LangGraph) within a containerized microservices architecture.

Principal ML Engineer

Key skills