ML Eng - Devops & AWS Focus

About the Role
We are seeking a Machine Learning Engineer with a strong DevOps mindset and deep experience in AWS cloud infrastructure. In this role, you will support the end-to-end ML lifecycle, focusing on operationalizing models, automating CI/CD pipelines, and maintaining scalable and secure infrastructure. You will collaborate with data scientists, software engineers, and DevOps teams to ensure efficient deployment and robust production environments for ML applications.

Key Responsibilities
Design, implement, and manage CI/CD pipelines for ML workflows using GitHub Actions and AWS-native tools.
Manage and optimize ML deployment infrastructure including Amazon EKS, EC2, ECS, and Lambda.
Establish and enforce code quality standards, including linting, unit testing, and security scanning.
Maintain and organize source control strategies, repository management, and versioning best practices.
Monitor and maintain build environments for reproducible and scalable model training and inference.
Collaborate with data science and engineering teams to containerize ML models and deploy via Docker and Kubernetes.
Implement infrastructure as code using Terraform or CloudFormation.
Apply DevOps and MLOps best practices for observability, logging, and alerting.

We are a company committed to creating inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity employer that believes everyone matters. Qualified candidates will receive consideration for employment opportunities without regard to race, religion, sex, age, marital status, national origin, sexual orientation, citizenship status, disability, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to Human Resources Request Form. The EEOC "Know Your Rights" Poster is available here.

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Required Skills & Qualifications
35+ years of experience in DevOps, ML Engineering, or Cloud Engineering roles.
Strong hands-on experience with AWS services: EKS, EC2, ECS, Lambda, S3, and CloudWatch.
Proven experience designing and maintaining CI/CD pipelines, particularly with GitHub Actions.
Proficient in Python, Bash, and scripting for automation.
Familiarity with containerization tools such as Docker and orchestration with Kubernetes.
Experience with infrastructure as code tools: Terraform, AWS CDK, or CloudFormation.
Solid understanding of code quality practices, secure development, and automated testing.
Strong communication skills and a collaborative approach to working across teams.

Preferred Qualifications
Experience in ML model deployment and MLOps frameworks (e.g., MLflow, SageMaker, Kubeflow).
Knowledge of software configuration and dependency management tools (e.g., Poetry, pipenv, virtualenv).
AWS certifications (e.g., DevOps Engineer, Machine Learning Specialty) are a plus.

Benefit packages for this role will start on the 31st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.