Principal AI/ML Engineering Lead
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- Contribute across the platform build: data ingestion, transformation, a canonical data model and entity resolution, an event backbone, configuration as code, and API and serving layers
- Strengthen DevOps and reliability: CI/CD, infrastructure as code, environment management, and observability across metrics, logs, traces, and service level objectives
- Partner with Security and IT: data classification and access control enforcement, secrets and key management, and controls aligned with HIPAA and HITRUST
- Lay the groundwork for the AI/ML platform: model serving and an inference gateway, feature and training data pipelines, model lifecycle (registry, evaluation, deployment, monitoring), and LLM integration with grounding and guardrails
- Keep protected health information inside controlled boundaries, favor in-VPC or local inference where required, and make sure that adding ML never opens a path for data to leak
- Work with existing team leads to continue raising the AI and ML fluency of the platform, security, and DevOps teams as you work alongside them, and set the patterns others will build on
- Additional responsibilities as needed
- Solid data engineering: SQL, pipeline tooling such as dbt, and data warehousing
- Bachelor’s Degree at a minimum
- 7-10+ years of experience in building and scaling production-grade machine learning or AI systems
- Strong cloud experience, ideally on Google Cloud (Vertex AI, BigQuery, Cloud Run, Pub/Sub, Cloud SQL, GKE), with AWS or Azure equivalents also welcome
- Substantial hands-on experience building and deploying AI/ML systems in production, including MLOps, model serving and inference infrastructure, feature and data pipelines, and integrating models into real applications
- DevOps and reliability skills: CI/CD, Terraform or a similar tool, containers and Kubernetes, and production observability
- Demonstrated experience as a generalist Software Engineer across backend, infrastructure, and data
- A security mindset and comfort working in a regulated environment with sensitive data. Familiarity with HIPAA or HITRUST is a plus
- Comfortable with ambiguity and a bias for shipping. You can go deep in one area and still move fluidly across the stack
- Experience with LLM applications, including retrieval, evaluation, guardrails, and knowledge graphs
- Experience with privacy enhancing technologies
- Experience with GraphQL or a federated serving layer
- Experience in healthcare or another regulated data domain
- Experience with entity resolution or master data management