We are seeking a Software Engineer with strong Python and MLOps foundations to build enhance and stabilize a productiongrade AI ML-ops platform supporting model training validation deployment and orchestration at enterprise scale
This role goes beyond scripting or notebookdriven ML You will work in a classbased APIdriven CICDenforced MLOps ecosystem contributing to reusable libraries ML workflows secure endpoints JSONschemadriven interfaces and automated pipelines aligned with Chevrons evolving GitHub Actions strategy
Key Responsibilities
Core Engineering MLOps
Design build and maintain productiongrade Python services using sound objectoriented principles SOLID separation of concerns reusability
Contribute to and extend the enterprise MLOps pipeline supporting
Model training validation and registration
Azure MLbased execution
Parameterized workflow orchestration
CICDdriven deployments
Implement and maintain ML inference endpoints with focus on
Highperformance IO using async programming and concurrency
Clean requestresponse contracts driven by JSON schemas
Robust validation and error handling
Develop welldefined APIs REST with
OpenAPI Swagger documentation
Versionaware schemas and backward compatibility considerations
Support platform evolution from Azure Pipelines to GitHub Actions contributing to
Pipeline rearchitecture
Build test and release automation
Secure artifact promotion across environments
Data ML Storage
Work with Pandas and Polars for feature handling transformations and data preparation
Support ML workflows leveraging ScikitLearn models and pipelines
Integrate with Azure Blob Storage and Azure Data Lake for model artifacts datasets and metadata
Nice to have Contribute to solutions involving Azure Cosmos DB for metadata or workflow state tracking
Quality Testing Maintainability
Write clean maintainable and testable codeoptimizing for longterm platform health over shortterm delivery speed
Expand and strengthen the automated test suite including
PyTestbased unit and integration tests
Validation of pipelines schemas and services
Advocate for and gradually adopt TestDriven Development TDD practices across the codebase
Actively reduce technical debt in a legacyheavy environment by