As a Principal ML Engineer, you will lead the technical architecture and engineering strategy for integrating sophisticated AI into high-stakes Healthcare Information Systems (HIS). We are looking for a seasoned builder who prioritizes reliability, system performance, and automated scalability over hype.
While many focus on the "science" of modeling, your mission is the engineering of the ecosystem. You will architect the robust MLOps pipelines and cloud infrastructure required to move models from experimental notebooks into mission-critical clinical environments. You are the bridge between raw data and resilient, production-grade AI services.
Key Responsibilities
Production Lifecycle: Lead the design and implementation of end-to-end ML lifecycles, focusing on automated CI/CD pipelines, model versioning (MLflow/DVC), and reproducible experimentation.
Inference at Scale: Architect high-performance serving layers for both LLMs and classical models, ensuring low-latency and high-availability in a secure healthcare cloud environment.
Agentic Orchestration: Build the underlying infrastructure for agent-based reasoning systems, ensuring these "Agentic" workflows are traceable, auditable, and integrated into existing HIS.
Data Reliability: Design robust data pipelines (ETL/ELT) to process healthcarespecific formats (FHIR, HL7, DICOM) into high-quality features for real-time and batch inference.
Hybrid Infrastructure: Manage and optimize cloud-native infrastructure (AWS/Azure/GCP) using Infrastructure as Code (Terraform/Pulumi) to support heavy
System Integrity: Implement comprehensive monitoring and observability frameworks to detect data drift, model decay, and system bottlenecks before they impact clinical outcomes.
Technical Leadership & Governance
Engineering Authority: Serve as the lead architect for the ML platform, ensuring all systems are HIPAA/HITRUST compliant and follow "security-by-design" principles. containerization (Docker/Kubernetes), and system documentation across the
Strategic Mentorship: Elevate the team by fostering a culture of "ML as Engineering," guiding junior engineers in building maintainable, modular, and scalable software.
Candidate Profile
Education & Experience
Academic Background: Master’s or PhD in Computer Science, Software Engineering, or a related technical field.
Proven Track Record: 10+ years of experience in software engineering, with at least 6 years dedicated to deploying and maintaining large-scale ML systems in production (not just research or POCs).
Core Technical Stack
MLOps & Cloud: Expert-level experience with Cloud Providers (AWS/GCP/Azure) and orchestration tools (Kubernetes, Kubeflow, or Airflow).
Engineering & Programming: Expert-level Python and Java/Go (or similar). Deep proficiency in backend frameworks and system design patterns.
Data Engineering: Strong experience with Spark, Snowflake/Databricks, and building scalable feature stores.
Applied AI: Hands-on experience deploying Generative AI (LLMs) and Agentic frameworks (LangChain/LangGraph) within a containerized microservices
Principal Edge (Preferred)
Hardware Optimization: Experience with GPU optimization, quantization, or specialized serving frameworks (vLLM, TGI).
Security & Compliance: Deep understanding of cybersecurity best practices within regulated industries (Healthcare, Finance, or Defense).
Distributed Systems: Proven ability to design systems that handle massive concurrency and distributed data processing.
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