Principal AI Engineer
Tasks
- Build LLM pipelines for clinical document processing
- Build Retrieval-Augmented Generation solutions
- Define AI evaluation and testing approaches
- Design and build agentic AI systems
- Develop solutions using Vertex AI
- Establish CI CD and MLOps pipelines
- Implement authentication authorization RBAC and governance
- Implement infrastructure-as-code
- Integrate with EHR and operational systems
- Mentor engineers and develop reusable design patterns
- Monitor latency reliability and hallucination risk
- Orchestrate multi-step workflows
- Provide forward deployed technical leadership
- Set up logging auditability and guardrails
Perks/Benefits
- N/A
Skills/Tech-stack
AI Agent Builder | AI Search | API | Agent Builder | Authentication | Authorization | Automated testing | BigQuery | CI/CD | Cloud Build | Cloud Run | Containers | Document AI | FHIR | FastAPI | GKE | Gemini | GitHub Actions | Go | Google Cloud | HIPAA | HL7 | HL7 FHIR | Infrastructure as Code | Kubernetes | Langchain | Langgraph | Logging | MLOps | Monitoring | OAuth | Observability | Prompt engineering | Prompt versioning | Python | RAG | RBAC | Retrieval-Augmented Generation | Telemetry | Terraform | Tracing | TypeScript | Vertex AI | Vertex AI Agent Builder | Vertex AI Search | “as-code”
Education
N/A
Roles
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