Lead ML Ops Developer
Skills
ArtifactsCVMContextDriftEnhanced Data Rates For GSM EvolutionGlueIdentity And Access Management
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Amazon SageMaker expert level across Studio Training Pipelines and Endpoints
- AWS IAM advanced with least-privilege policy design from scratch
- Amazon S3 advanced including lifecycle encryption and bucket policies
- AWS KMS working knowledge for ML key management
- CI/CD tooling advanced for automated model promotion across environments
- Python and PySpark expert with production-quality code
- Statistical and parity testing methodology advanced for migrated models
- MLOps pattern design expert including registries monitoring and governance gates
- Git and version control advanced including branching PR workflows and release tagging
- Track record of technical ownership for shared platforms
- Enablement mindset to create self-serve patterns
- Risk communication for non-technical stakeholders
- Decision-making under ambiguity with iterative delivery
Nice to have
- Lead ownership
- Enablement mindset
- Risk communication
- Decision making under ambiguity
Day to day
- Own the design and delivery of a production-grade MLOps platform on AWS for machine learning workloads.
- Lead the migration of a 12-model production suite from legacy infrastructure to SageMaker and define parity testing and sign-off methods.
- Build CI/CD pipelines and governance controls while producing clear documentation runbooks and migration risk updates for stakeholders.
Hiring process
- Call with Talent Acquisition Team
- Role specific capability interview
- Possible short presentation practical task or values conversation if needed