Sr. Cloud DevOps Engineer

MedeAnalytics - Richardson, TX

Full-time - Senior

Richardson, TX

Publishing Industries

About the position

MedeAnalytics is seeking a highly motivated Senior Cloud DevOps Engineer with a passion for AI, data science, and cloud automation to join our Cloud Engineering team. This lead role will drive automation initiatives aligned with our R&D strategy, support cloud migrations, and manage the cloud infrastructure in a SaaS environment. You will collaborate with product development to design and maintain scalable, reliable, and secure solutions, ensuring best practices in DevOps and cloud computing. If you thrive in a fast-paced, innovative environment and are committed to improving healthcare outcomes, we encourage you to apply.

Responsibilities

• Design, implement, and maintain automated infrastructure provisioning and management using tools like Terraform and AWS CloudFormation.
• Collaborate with development teams to automate deployment and testing processes, including AI and data science models.
• Manage and optimize Kubernetes clusters on AWS.
• Develop and maintain Helm charts for packaging and deploying applications, including AI and data science models.
• Build and maintain robust CI/CD pipelines using tools like Jenkins, GitLab CI/CD, ArgoCD, Atlantis or CircleCI, tailored for AI and data science workflows.
• Integrate automated testing frameworks for both application code and AI models.
• Implement code quality, security checks, and model validation within the pipelines.
• Manage and optimize AWS cloud resources, including EC2 instances, S3 buckets, VPCs, and other services, with a focus on supporting AI and data science workloads.
• Implement best practices for cloud security, cost optimization, and performance tuning.
• Monitor and troubleshoot cloud infrastructure issues, particularly related to AI and data science applications.
• Implement comprehensive monitoring solutions (e.g., Prometheus, Grafana, CloudWatch) to track system performance, AI model health, and data quality.
• Configure alerts and notifications to ensure timely response to critical issues, including model drift or performance degradation.
• Collaborate with data scientists to develop and deploy AI models into production.
• Implement MLops practices to manage the entire lifecycle of AI models, including versioning, experimentation, and reproducibility.
• Use tools like Kubeflow, MLflow, or Airflow to automate ML workflows.
• Ensure data privacy and security compliance within AI and data science pipelines.
• Work closely with development, data science, and AI teams to understand their requirements and provide technical guidance.
• Collaborate with other DevOps team members to share knowledge and best practices, particularly related to AI and data science.
• Identify and resolve complex technical challenges, including those specific to AI and data science applications.

Requirements

• Bachelor's degree in computer science, Engineering, or a related field.
• 3+ years of experience as a DevOps Engineer or a similar role, with a focus on AI and data science.
• Certification in AWS (Amazon Web Services) is required, demonstrating a strong understanding of cloud architecture, services, and best practices.
• Kubernetes certification (CKA or CKAD) is required, showcasing expertise in container orchestration, deployment, and management at scale.
• Strong proficiency in AWS cloud services and tools.
• Experience with Terraform and AWS CloudFormation for infrastructure automation.
• In-depth knowledge of Kubernetes and containerization technologies (Docker).
• Experience with Helm charts and CI/CD pipelines, tailored for AI and data science workflows.
• Understanding of scripting languages (e.g., Bash, Python).
• Excellent problem-solving and troubleshooting skills.
• Strong communication and collaboration abilities.

Nice-to-haves

• Certification in AWS (e.g., AWS Certified DevOps Engineer)
• Experience with serverless computing (e.g., AWS Lambda, EKS)
• Knowledge of security best practices and compliance frameworks
• Experience with microservices architecture
• Familiarity with data engineering concepts and tools
• Experience with Jenkins, ArgoCD and Atlantis for GitOps-based deployments
• Understanding of healthcare data and regulatory compliance (e.g., HIPAA)
• Experience with AI and data science frameworks (e.g., TensorFlow, PyTorch).
• Knowledge of MLops principles and tools.