Software EngineerPython and MLOps
Share
Job Location:
Monthly Salary:
Posted on:
2 hours ago
Vacancies:
1 Vacancy
Job Summary
Experience Range - 8-10 years
We are seeking a Software Engineer with strong Python and MLOps foundations to build enhance and stabilize a production grade AI/ML platform supporting model training validation deployment and orchestration at enterprise scale.
This role goes beyond scripting or notebook driven ML.
Key Responsibilities
Core Engineering & MLOps
-High performance I/O using async programming and concurrency
-Clean request/response contracts driven by JSON schemas
-Robust validation and error handling
Develop well defined APIs (REST) with:
-OpenAPI / Swagger documentation
-Version aware schemas and backward compatibility considerations
Support platform evolution from Azure Pipelines to GitHub Actions contributing to:
-Pipeline re architecture
-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 Scikit Learn 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.
We are seeking a Software Engineer with strong Python and MLOps foundations to build enhance and stabilize a production grade AI/ML platform supporting model training validation deployment and orchestration at enterprise scale.
This role goes beyond scripting or notebook driven ML.
Key Responsibilities
Core Engineering & MLOps
- Design build and maintain production grade Python services using sound object oriented principles (SOLID separation of concerns reusability).
- Model training validation and registration
- Azure ML based execution
- Parameterized workflow orchestration
- CI/CD driven deployments
-High performance I/O using async programming and concurrency
-Clean request/response contracts driven by JSON schemas
-Robust validation and error handling
Develop well defined APIs (REST) with:
-OpenAPI / Swagger documentation
-Version aware schemas and backward compatibility considerations
Support platform evolution from Azure Pipelines to GitHub Actions contributing to:
-Pipeline re architecture
-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 Scikit Learn 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.
