Cloud MLOps Engineer – AWS Focus

At Zelis, we Get Stuff Done. So, let’s get to it! 

A Little About Us 

Zelis is modernizing the healthcare financial experience for all by providing a connected platform that bridges the gaps and aligns interests across payers, providers, and healthcare consumers. This platform serves more than 750 payers, including the top 5 national health plans, BCBS insurers, regional health plans, TPAs and self-insured employers, and millions of healthcare providers and consumers. Zelis sees across the system to identify, optimize, and solve problems holistically with technology built by healthcare experts—driving real, measurable results for clients. 

A Little About You 

You bring a unique blend of personality and professional expertise to your work, inspiring others with your passion and dedication. Your career is a testament to your diverse experiences, community involvement, and the valuable lessons you've learned along the way. You are more than just your resume; you are a reflection of your achievements, the knowledge you've gained, and the personal interests that shape who you are.

Key Responsibilities:

• Architect, build, and maintain automated machine learning deployment pipelines in AWS.

• Collaborate with Data Scientists and ML Engineers to productionize models and manage the full ML model lifecycle (build, deploy, monitor, retrain).

• Implement continuous training (CT) and continuous integration/continuous deployment (CI/CD) practices for machine learning.

• Monitor model performance, detect drift, and automate alerts and retraining workflows.

• Build scalable, secure, and fault-tolerant infrastructure using services like AWS SageMaker, EKS, Lambda, S3, EC2, CloudFormation, and/or Terraform.

• Develop and maintain model versioning, governance, and auditing processes.

• Implement best practices in monitoring, logging, and security for machine learning applications.

• Serve as a bridge between DevOps, Data Science, and Cloud Engineering teams, ensuring alignment and operational excellence.

What You’ll Bring to Zelis

Required Skills and Experience:

• 3+ years of experience working in DevOps, Cloud Engineering, or MLOps roles.

• 2+ years specifically supporting machine learning production environments.

• Hands-on experience with AWS cloud services — particularly SageMaker, EKS, S3, IAM, CloudWatch.

• Strong proficiency with Python and scripting for automation (Bash, etc.).

• Experience with containerization (Docker) and orchestration (Kubernetes).

• Expertise in building CI/CD pipelines for ML models using tools like GitHub Actions, CodePipeline, Jenkins, or similar.

• Familiarity with ML lifecycle management tools such as MLflow, SageMaker Pipelines, Kubeflow, or equivalent.

• Deep understanding of model monitoring, model drift detection, and retraining workflows.

• Strong appreciation for security, reliability, and scalability in production systems.

Preferred Qualifications:

• Experience with Infrastructure as Code (Terraform, AWS CloudFormation).

• Knowledge of data versioning tools (e.g., DVC).

• Experience with event-driven architectures using AWS Lambda and SQS.

• Familiarity with monitoring stacks (Prometheus, Grafana, CloudWatch Insights).

What This Role Is Not:

• Not a traditional DevOps engineer role focused purely on application development pipelines or server maintenance.

• Not a Data Scientist or ML research role; this is about operationalizing models — not building or training them.

• Not an AWS SysAdmin or "cloud generalist" position; hands-on experience specifically supporting machine learning deployments is required.

• Not an entry-level cloud role; we require experience with production systems supporting AI/ML workflows.

If you're passionate about scaling machine learning systems and building world-class MLOps capabilities, we would love to hear from you.