Principal ML Engineer

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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 healthcare-specific 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 compute workloads.
System Integrity: Implement comprehensive monitoring and observability frameworks to detect data drift, model decay, and system bottlenecks before they impact clinical outcomes.

Engineering Authority: Serve as the lead architect for the ML platform, ensuring all systems are HIPAA/HITRUST compliant and follow "security-by-design" principles.
Operational Excellence: Establish rigorous standards for code quality, containerization (Docker/Kubernetes), and system documentation across the engineering organization.
Strategic Mentorship: Elevate the team by fostering a culture of "ML as Engineering," guiding junior engineers in building maintainable, modular, and scalable software.

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 (not just research or POCs).

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 architecture.

Hardware Optimization: Experience with GPU optimization, quantization, or specialized serving frameworks (vLLM, TGI).
Master’s or PhD in Computer Science, Software Engineering, or a related technical field is preferred
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.