Founded in 1993, MedeAnalytics is an innovation-focused company. Over the past three decades, we have worked tirelessly to reimagine healthcare through the power of data—and helped thousands of organizations achieve their potential along the way.

Leveraging state-of-the-art analytics and data activation, MedeAnalytics delivers actionable insights that support payers, providers, employers, and public entities as they navigate the complex healthcare landscape.

Using artificial intelligence and machine learning alongside the most advanced data orchestration in the industry, we empower organizations to optimize their resource allocation, experience superior patient outcomes, and achieve population health management goals.

And that’s just the beginning.

With a deep understanding of the complex challenges facing the healthcare industry,

MedeAnalytics offers a comprehensive suite of solutions to address key areas such as:

Population Health Management: Gain insights into patient populations, identify at-risk individuals, and implement targeted interventions to improve health outcomes. Value-Based Care: Optimize care delivery, reduce costs, and enhance patient satisfaction by aligning with value-based care models. Revenue Cycle Management: Streamline revenue cycle processes, improve reimbursement rates, and minimize denials. And more…

MedeAnalytics is committed to delivering cutting-edge technology and exceptional customer service. Our team is passionate about transforming healthcare and making a positive impact on the lives of patients.

About the job:

Location Requirement: The candidate hired for this position must be based within a commutable distance from Nashville, TN, or Richardson, TX. This role may require periodic in-office attendance, and applicants not located within proximity to these areas may not be considered.

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.

Essential Duties and Responsibilities:

Infrastructure Automation: 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. Containerization and Orchestration: Manage and optimize Kubernetes clusters on AWS. Develop and maintain Helm charts for packaging and deploying applications, including AI and data science models. Implement containerization strategies using Docker or other relevant technologies. CI/CD Pipelines: 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. Cloud Infrastructure Management: 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. Monitoring and Alerting: 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. AI and Data Science MLops: 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. Collaboration and Problem-Solving: 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.

Key Required Qualifications:

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.

Preferred Qualifications:

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.

Benefits Include:

Comprehensive Medical, Dental, and Vision Coverage – Effective the first of the month following your start date Company-Paid Life & AD&D Insurance, plus Short-Term and Long-Term Disability (STD/LTD) Company-Paid Employee Assistance Program (EAP) premium tier for your wellbeing 401(k) Plan with company match Paid Holidays and Paid Time Off (PTO) Accruals Employee Referral Bonus Program Professional Development Opportunities to support your growth And More!

We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, national origin, gender, sex, gender identity or expression, sexual orientation, age, citizenship, marital or parental status, disability, veteran status, or other class protected by applicable law. We are proud to be an equal opportunity workplace.

** Currently, we are not able to offer sponsorship or take over sponsorship to candidates who are not eligible to work in the country where the position is located.

At MedeAnalytics we deeply value each and every one of our committed, inspired and passionate team members. If you're looking to make an impact doing work that matters, you're in the right place. Help us shape the future of healthcare by joining #TeamMede.

MedeAnalytics does not utilize any outside vendors/agencies.  Please no unsolicited phone calls or invites.