Job Summary
Description: Work Timings: 01:30 - 10:30 PM Monday (WFH), Tuesday-Friday(WFO) Role 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. You will work in a class based, API driven, CI/CD enforced MLOps ecosystem, contributing to reusable libraries, ML workflows, secure endpoints, JSON schema driven interfaces, and automated pipelines aligned with Chevron’s evolving GitHub Actions strategy. Key Responsibilities Core Engineering & MLOps - Design, build, and maintain production grade Python services using sound object oriented principles (SOLID, separation of concerns, reusability). Contribute to and extend the enterprise MLOps pipeline supporting: - Model training, validation, and registration - Azure ML–based execution - Parameterized workflow orchestration - CI/CD driven deployments Implement and maintain ML inference endpoints, with focus on: -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. Quality, Testing & Maintainability Write clean, maintainable, and testable code—optimizing for long term platform health over short term delivery speed. Expand and strengthen the automated test suite, including: -PyTest based unit and integration tests -Validation of pipelines, schemas, and services Advocate for and gradually adopt Test Driven Development (TDD) practices across the codebase. Actively reduce technical debt in a legacy heavy environment by: -Refactoring duplicated or brittle code -Introducing shared libraries and abstractions -Improving documentation and developer ergonomics Required Technical Skills Advanced Python -Class based design, modular architecture, reusable components -Not “script only” or notebook centric development API Development -RESTful services, endpoint performance tuning -OpenAPI / Swagger documentation Async Programming -Async/await, concurrency, threading for I/O heavy workloads CI/CD -Azure Pipelines and/or GitHub Actions -Experience evolving pipelines, not just consuming them Data & ML -Pandas / Polars -Scikit Learn Testing -PyTest -Strong appreciation for automated testing as a quality gate Schema Driven Development -JSON schemas for configuration, workflow parameters, and APIs -Experience updating and managing schema evolution Nice to Have Skills Azure ML SDK (training, pipelines, model registration) Pydantic for request/response validation Azure Cosmos DB Schema versioning strategies Experience with large, shared enterprise codebases Desired Qualities & Mindset Takes Initiative -Understands how individual stories connect to platform level objectives. -Proactively improves systems instead of waiting for direction. Strong Debugger -Comfortable tracing failures across pipelines, services, storage, and infrastructure. -Attempts root cause analysis before escalating issues. Values Code Qua
Key Responsibilities
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Skill Requirements
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Other Requirements
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