Senior MLOps Engineer

Location: Atlanta, GA or Minneapolis, MN 
Time Zone: Eastern Time (ET)

Job Description: 

We are seeking a Senior MLOps Engineer with deep expertise in AWS CDK, MLOps, and Data Engineering tools to join a high-impact team focused on building reusable, scalable deployment pipelines for Amazon SageMaker workloads. This role combines hands-on engineering, automation, and infrastructure expertise with strong stakeholder engagement skills. You will work closely with Data Scientists, ML Engineers, and platform teams to accelerate ML productization using best-in-class DevOps practices. 

 Key Responsibilities: 

• Design, implement, and maintain reusable CI/CD pipelines for SageMaker-based ML workflows. 

• Develop Infrastructure as Code using AWS CDK for scalable and secure cloud deployments. 

• Build and manage integrations with AWS Lambda, Glue, Step Functions, and OpenTable formats (Apache Iceberg, Parquet, etc.). 

• Support MLOps lifecycle: model packaging, deployment, versioning, monitoring, and rollback strategies. 

• Use GitLab to manage repositories, pipelines, and infrastructure automation. 

• Enable logging, monitoring, and cost-effective scaling of SageMaker instances and jobs. 

• Collaborate closely with stakeholders across Data Science, Cloud Platform, and Product teams to gather requirements, communicate progress, and iterate on infrastructure designs. 

• Ensure operational excellence through well-tested, reliable, and observable deployments. 

 Required Skills: 

• 2+ years of experience in MLOps, with 4+ years of experience in DevOps or Cloud Engineering, ideally with a focus on machine learning workloads. 

• Hands-on experience with GitLab CI Pipelines, artifact scanning, vulnerability checks, and API management. 

• Experience in Continuous Development, Continuous Integration (CI/CD), and Test-Driven Development (TDD). 

• Experience in building microservices and API architectures using FastAPI, GraphQL, and Pydantic. 

• Proficiency in Python v3.6 or higher and experience with Python frameworks such as Pytest. 

• Strong experience with AWS CDK (TypeScript or Python) for IaC. 

• Hands-on experience with Amazon SageMaker, including pipeline creation and model deployment. 

• Solid command over AWS Lambda, AWS Glue, OpenTable formats (like Iceberg/Parquet), and event-driven architectures. 

• Practical knowledge of MLOps best practices: reproducibility, metadata management, model drift, etc. 

• Experience deploying production-grade data and ML systems. 

• Comfortable working in a consulting/client-facing environment, with strong stakeholder management and communication skills

Preferred Qualifications: 

• Experience with feature stores, ML model registries, or custom SageMaker containers. 

• Familiarity with data lineage, cost optimization, and cloud security best practices. 

• Background in ML frameworks (TensorFlow, PyTorch, etc.). 

 Education: 

• Bachelor’s or master’s degree in any of the following: statistics, data science, computer science, or another mathematically intensive field.