Principal AI/ML Engineering Lead

About This Opportunity

Our talent team is representing a confidential healthcare technology organization that is rebuilding its data platform with strong emphasis on patient data protection, governance, and self-service analytics. AI and ML are central to their future product strategy, and they need a hands-on engineering leader to ensure these capabilities can be added safely and quickly to their HIPAA-compliant infrastructure.

This Principal AI/ML Engineering Lead role offers the opportunity to shape the technical foundation of a platform serving healthcare members at scale, working across data pipelines, ML infrastructure, and secure model deployment.

Responsibilities

• Contribute across the platform build: data ingestion, transformation, canonical data modeling and entity resolution, event backbone architecture, configuration as code, and API and serving layers
• Strengthen DevOps and reliability practices including CI/CD, infrastructure as code, environment management, and comprehensive observability across metrics, logs, traces, and service level objectives
• Partner with Security and IT teams on data classification, access control enforcement, secrets and key management, and controls aligned with HIPAA and HITRUST requirements
• Build the foundational AI/ML platform components: model serving and inference gateway, feature and training data pipelines, model lifecycle management (registry, evaluation, deployment, monitoring), and LLM integration with grounding and guardrails
• Ensure protected health information remains within controlled boundaries, favor in-VPC or local inference where required, and prevent data leakage pathways when adding ML capabilities
• Raise AI and ML fluency across platform, security, and DevOps teams through hands-on collaboration and by establishing patterns others will build upon

What We're Looking For

Required:

• Bachelor's degree at minimum
• 7-10+ years of experience building and scaling production-grade machine learning or AI systems
• Demonstrated generalist Software Engineering experience across backend, infrastructure, and data domains
• Substantial hands-on experience with MLOps, model serving and inference infrastructure, feature and data pipelines, and integrating models into production applications
• Strong cloud platform experience, ideally Google Cloud (Vertex AI, BigQuery, Cloud Run, Pub/Sub, Cloud SQL, GKE); AWS or Azure equivalents also welcome
• DevOps and reliability skills: CI/CD, Terraform or similar IaC tools, containers and Kubernetes, production observability
• Solid data engineering foundation: SQL, pipeline tooling such as dbt, data warehousing concepts
• Security mindset and comfort working in regulated environments with sensitive data; familiarity with HIPAA or HITRUST is a plus
• Experience with LLM applications including retrieval, evaluation, guardrails, and knowledge graphs
• Comfortable with ambiguity and biased toward shipping; able to go deep in one area while moving fluidly across the stack
• Must be authorized to work in the United States; visa sponsorship is not available
• Must be able to work remotely from within the United States

Preferred:

• Experience with entity resolution or master data management
• Background in healthcare or another regulated data domain
• Experience with GraphQL or federated serving layers
• Familiarity with privacy enhancing technologies

How We Work With You

When you apply through Get A Job.ai, one of our recruiters will screen your background and experience. If there's a strong match, we'll submit your profile to our client for consideration. Please do not contact the client directly—we manage all communication and coordination to give you the best chance at this confidential opportunity.

Pay

Compensation details will be discussed during the screening process based on experience and qualifications.

Equal Employment Opportunity: Get A Job.ai is committed to creating a diverse and inclusive environment. We provide equal employment opportunity to all qualified applicants regardless of race, color, ancestry, religion, gender, gender identity or expression, sexual orientation, marital status, national origin, genetics, disability, age, or veteran status.