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 Quality
-Will not compromise maintainability for speed.
-Actively resists adding to technical debt.
Engineering Craftsmanship
-Cares about design, clarity, and long term scalability.
-Sees MLOps as software engineering, not just ML enablement.
Advocates for Better Practices
-Encourages testability, consistency, and clean abstractions—even in legacy environments.
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