Senior Software Engineer - MLOps/Python

Key Responsibilities :

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 :

1. Model training

2. Model validation

3. Model registration

4. Azure ML execution environments

5. Parameterized workflow orchestration

6. 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.

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.

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.

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 :

1. Datasets

2. Model artifacts

3. 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.

MLOps & Machine Learning :

- Experience designing and supporting production MLOps platforms.

- Understanding of ML lifecycle management and model operationalization.

- Experience with :

1. Scikit-Learn

2. Model training workflows

3. Model deployment processes

4. Model validation frameworks

API Development :

- REST API design and implementation.

- OpenAPI/Swagger specification development.

- API versioning and lifecycle management.

- Performance optimization and endpoint scalability.

Async Programming :

- Strong experience with :

1. Async/Await

2. Concurrency

3. Multithreading

4. I/O optimization techniques

CI/CD & DevOps :

- Hands-on experience with :

1. Azure Pipelines

2. GitHub Actions

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

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

Data Processing :

- Pandas

- Polars

- Data transformation and feature engineering

- Large-scale data handling

Schema-Driven Development :

- JSON Schema design and maintenance.

- Schema validation and evolution.

- API contract management.

- Configuration-driven architecture.

Must-Have Skills :

- 7+ years of experience in Python Development and MLOps

- Engineering Strong experience in Modeling Automation and ML pipeline development

- Expertise in MLOps frameworks and model lifecycle management

- Hands-on experience with model training pipelines and workflow orchestration

- Experience in artifact versioning and model registry management

- Strong knowledge of GCP Vertex AI or Azure ML Experience with CI/CD pipelines for ML deployments

- Proficiency in Docker, Kubernetes, and cloud-native deployments Strong scripting, automation, and debugging skills

- Experience with Git, ML experiment tracking, and monitoring tools

Good-to-Have Skills :

- Experience with MLflow, Kubeflow, Airflow, or Prefect Exposure to LLMOps / Generative AI deployment workflows

- Knowledge of Terraform or Infrastructure as Code (IaC)

- Familiarity with Databricks or Spark-based ML workloads Experience with feature stores and data versioning

- Exposure to AWS SageMaker Agile/Scrum development experience Strong understanding of scalable AI platform architecture

Good to Have Skills :

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