Senior Software Engineer - MLOps

Job Description :

About the Role :

We are seeking a highly skilled Senior Software Engineer - MLOps (Python) to design, build, enhance, and stabilize a production-grade enterprise AI/ML platform that supports model training, validation, deployment, orchestration, and lifecycle management at scale.

This role is ideal for a software engineer who views MLOps as a disciplined software engineering practice rather than a collection of scripts or notebooks. The successful candidate will contribute to a class-based, API-driven, CI/CD-enforced MLOps ecosystem, focusing on reusable libraries, ML workflows, secure endpoints, schema-driven interfaces, automated testing, and cloud-native deployment strategies.

You will play a key role in modernizing platform capabilities, supporting the transition from Azure Pipelines to GitHub Actions, and improving platform scalability, maintainability, and engineering excellence.

Key Responsibilities :

1. Core Engineering & MLOps Development :

- Design, develop, and maintain production-grade Python services using object-oriented programming principles and software design best practices.

- Build reusable, scalable, and maintainable components aligned with SOLID principles and separation of concerns.

- Extend and enhance enterprise MLOps frameworks supporting :

i. Model training

ii. Model validation

iii. Model registration

iv. Azure ML execution environments

v. Parameterized workflow orchestration

vi. CI/CD-driven deployment pipelines

- Develop and maintain high-performance ML inference services and APIs.

- Implement robust validation, exception handling, and fault-tolerant processing mechanisms.

- Build clean, schema-driven request/response interfaces using JSON schemas and validation frameworks.

- Create and maintain versioned APIs with backward compatibility considerations.

- Contribute to platform modernization initiatives, including migration from Azure Pipelines to GitHub Actions.

2. API & Service Engineering :

- Design and implement RESTful APIs for enterprise AI/ML applications.

- Develop OpenAPI/Swagger-compliant API documentation.

- Optimize endpoint performance, scalability, and reliability.

- Implement asynchronous processing using Python async/await patterns.

- Build concurrent processing solutions for I/O-intensive workloads.

3. CI/CD & Platform Automation :

- Design and improve automated build, test, release, and deployment pipelines.

- Contribute to GitHub Actions adoption and pipeline re-architecture.

- Enable secure artifact management and promotion across development, testing, and production environments.

- Automate deployment and operational workflows across ML platform components.

4. Data Engineering & Machine Learning Integration :

- Utilize Pandas and Polars for data processing, feature engineering, and transformations.

- Support ML workflows utilizing Scikit-Learn models and pipelines.

- Integrate with Azure Blob Storage and Azure Data Lake for :

i. Datasets

ii. Model artifacts

iii. Metadata management

- Collaborate with data science and platform teams to operationalize machine learning models.

- Support metadata and workflow management solutions utilizing Azure Cosmos DB where applicable.

5. Quality Engineering & Testing :

- Develop clean, maintainable, and highly testable software.

- Expand automated testing coverage using :

i. PyTest unit testing

ii. Integration testing

iii. API validation testing

iv. Schema validation testing

- Champion engineering quality standards and promote Test-Driven Development (TDD) practices.

- Ensure all code changes meet quality, reliability, and maintainability standards before deployment.

6. Technical Debt Reduction & Platform Improvement :

- Refactor legacy code to improve scalability, readability, and maintainability.

- Eliminate duplicated code through shared libraries and reusable abstractions.

- Improve developer experience through enhanced tooling, documentation, and automation.

- Drive engineering best practices across the MLOps platform ecosystem.

Required Technical Skills :

1. Python Development (Mandatory) :

- Advanced Python programming expertise.

- Strong object-oriented design and development experience.

- Experience building modular, reusable, enterprise-scale applications.

- Strong understanding of software architecture patterns and clean code principles.

2. MLOps & Machine Learning :

- Experience designing and supporting production MLOps platforms.

- Understanding of ML lifecycle management and model operationalization.

- Experience with :

i. Scikit-Learn

ii. Model training workflows

iii. Model deployment processes

iv. Model validation frameworks

3. API Development :

- REST API design and implementation.

- OpenAPI/Swagger specification development.

- API versioning and lifecycle management.

- Performance optimization and endpoint scalability.

4. Async Programming :

- Strong experience with :

i. Async/Await

ii. Concurrency

iii. Multithreading

iv. I/O optimization techniques

5. CI/CD & DevOps :

- Hands-on experience with :

i. Azure Pipelines

ii. GitHub Actions

- Experience designing, enhancing, and modernizing CI/CD pipelines.

- Build, test, release, and deployment automation expertise.

6. Data Processing :

- Pandas.

- Polars.

- Data transformation and feature engineering.

- Large-scale data handling.

7. Testing & Quality Assurance :

- PyTest.

- Unit testing.

- Integration testing.

- Automated quality gates.

- Test automation frameworks.

8. Schema-Driven Development :

- JSON Schema design and maintenance.

- Schema validation and evolution.

- API contract management.

- Configuration-driven architecture.

Preferred / Nice-to-Have Skills :

- Azure Machine Learning SDK.

- Azure ML Training Pipelines.

- Model Registration Frameworks.

- Pydantic.

- Azure Cosmos DB.

- Schema Versioning Strategies.

- Enterprise Platform Engineering.

- Large Shared Codebase Management.

- Cloud-Native ML Platform Architecture.

- GitHub Enterprise Workflows.

Desired Candidate Profile :

1. Initiative & Ownership :

- Demonstrates ownership beyond assigned tasks.

- Understands platform-level objectives and business outcomes.

- Proactively identifies and implements improvements.

2. Strong Problem Solver :

- Skilled at troubleshooting across applications, pipelines, infrastructure, and storage layers.

- Performs thorough root-cause analysis before escalation.

3. Engineering Excellence :

- Prioritizes maintainability, reliability, and scalability.

- Consistently produces clean, well-structured code.

- Applies software engineering discipline to MLOps environments.

4. Quality-Focused Mindset :

- Advocates automated testing and continuous quality improvement.

- Resists shortcuts that increase technical debt.

- Promotes sustainable engineering practices.

5. Collaboration & Influence :

- Works effectively with Data Scientists, Platform Engineers, DevOps Engineers, and Product Teams.

- Influences adoption of best practices through technical leadership.

- Drives consistency and standardization across engineering teams.

Educational Qualifications :

- Bachelor's or Master's degree in Computer Science, Information Technology, Software Engineering, Data Science, or a related discipline.

- Equivalent industry experience will also be considered.