AWS Eng - AI Exposure

About the Role
We are seeking a Senior AWS Cloud Engineer with strong expertise in building and managing cloud infrastructure to support AI/ML initiatives. You will play a critical role in designing, deploying, and optimizing scalable, secure, and automated cloud solutions in AWS, with a focus on enabling machine learning workflows. This role requires a blend of cloud engineering, DevOps, and AI/ML lifecycle understanding.

Key Responsibilities
Design, build, and manage scalable cloud infrastructure on AWS with Infrastructure as Code (IaC) using Terraform.
Collaborate with data science and ML teams to support the full ML lifecycle, including model training, deployment, and monitoring.
Deploy and manage AWS services such as SageMaker, Step Functions, EKS, and Lambda for AI/ML workflows.
Implement and manage ML experiment tracking using MLflow.
Write automation scripts and utilities using Python.
Manage CI/CD pipelines using GitHub Actions.
Work closely with Agile teams, using Jira for tracking and sprint planning.
Champion DevOps and cloud best practices, including cost optimization, monitoring, and security.

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
5+ years of experience in cloud engineering, with a strong focus on AWS.
Solid hands-on experience with Terraform and Infrastructure as Code.
Strong understanding of the AI/ML lifecycle, with the ability to support model development and deployment workflows.
Experience with key AWS services related to machine learning: SageMaker, Step Functions, EKS, Lambda.
Proficient in Python scripting for automation and tooling.
Experience with MLflow for tracking experiments and model management.
Familiarity with Agile methodologies and tools like Jira.
Proficiency with GitHub and GitHub Actions for version control and CI/CD automation.
Strong communication skills and a proactive, collaborative mindset.

Preferred Qualifications
AWS certifications (e.g., AWS Certified DevOps Engineer, AWS Certified Machine Learning Specialty).
Experience in regulated or high-compliance environments.
Exposure to monitoring/logging tools and security best practices in cloud environments.

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.