Senior MLOps Engineer

Job Description

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.