Principal ML Engineer
Tasks
- Architect inference serving for low latency high availability
- Build automated CI/CD pipelines
- Build infrastructure for traceable auditable agentic reasoning
- Deploy generative AI and agentic workflows in production
- Design end to end ML lifecycles
- Design healthcare data pipelines using ETL ELT
- Ensure HIPAA and HITRUST compliant security by design
- Implement FHIR HL7 DICOM data processing
- Implement model versioning and reproducible experimentation
- Lead ML platform architecture
- Manage cloud infrastructure using infrastructure as code
- Mentor engineers for scalable maintainable ML engineering
- Set up monitoring and observability for drift and bottlenecks
- Standardize code quality containerization and documentation
Perks/Benefits
Skills/Tech-stack
AWS | Apache Airflow | Azure | CI/CD | DICOM | DVC | Data Drift | Data drift monitoring | Databricks | Docker | Docker Swarm | Drift monitoring | ELT | ETL | FHIR | Feature Engineering | Feature Store | GPU Optimization | Generative AI | Go | Google Cloud | HIPAA | HITRUST | HL7 | Infrastructure as Code | Java | Kubeflow | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | MLOps | MLflow | Microservices | Model Decay Monitoring | Model decay | Observability | Pulumi | Python | Quantization | Snowflake | Spark | TGI | Terraform | VLLM | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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