Senior MLOps Engineer - Production Environments (4-7 yrs)

Position Overview :

- Contribute to the development and maintenance of end-to-end machine learning pipelines, from data ingestion and preprocessing to model training, deployment, and monitoring.

- Build and optimize infrastructure for rapid prototyping, deployment, and evaluation of machine learning models.

- Collaborate with cross-functional teams to ensure seamless integration of machine learning solutions into production systems.

- Support monitoring and troubleshooting of machine learning models and pipelines to ensure scalability and performance in production.

- Implement and support CI/CD pipelines for machine learning workflows, including automated testing and model versioning.

- Help maintain and enhance the platform for deploying standard and custom-trained AI models for clients.

- Engage in project planning and scoping to align system designs with team objectives and business requirements.

- Contribute to setting and promoting best practices for code quality and software development across the data science organization.

A little bit about you :

- Experience deploying and maintaining machine learning models in production environments.

- Fluency in Python, including general-purpose programming and working with libraries such as scikit-learn, TensorFlow, or PyTorch.

- Solid understanding of traditional software engineering principles, including version control, unit testing, code reviews, and design patterns, to ensure maintainability and scalability of machine learning systems.

- Familiarity with REST API design and implementation, preferably using frameworks such as Flask or FastAPI.

- Experience with cloud platforms (preferably AWS) and tools like SageMaker or equivalent.

- Experience with modern containerization tools such as Docker and Kubernetes.

- Familiarity with CI/CD practices and tooling, such as GitHub Actions and Jenkins.

- Strong communication skills and the ability to collaborate effectively with a diverse range of teams, including product managers, engineers, and data scientists.

- Experience building and maintaining data pipelines with a focus on scalability and performance.

- A foundational understanding of model lifecycle concepts, including model retraining, versioning, and monitoring.