Senior MLOps Engineer

What youll be doing

• Maintain and enhance existing ML pipelines in AWS with a focus on Infrastructure as Code using CloudFormation.
• Implement minimal but essential pipeline extensions to support ongoing data science workstreams.
• Document infrastructure usage, architecture, and design using tools like Confluence, GitHub Wikis, and system diagrams.
• Act as the internal infrastructure expert, collaborating with data scientists to guide and support model deployments.
• Research and implement optimization strategies for ML workflows and infrastructure.
• Work independently and collaboratively with cross-functional teams to support ML product

What do we need from you

• 5+ years of hands-on DevOps experience with AWS Cloud.
• Proven experience with at least two MLOps projects deployed using SageMaker or similar AWS services.
• Strong proficiency in AWS services: SageMaker, ECR, S3, Lambda, Step Functions.
• Expertise in Infrastructure as Code using CloudFormation for dev/test/prod environments.
• Solid understanding of MLOps best practices and Data Science principles.
• Proficient in Python for scripting and automation.
• Experience building and managing Docker images.
• Hands-on experience with Git-based version control systems such as AWS CodeCommit or GitHub, including GitHub Actions for CI/CD pipelines.