Cloud Engineer with AI Focus

Qualifications

• This role requires strong proficiency in AWS cloud services and tools, including Terraform and AWS CloudFormation for infrastructure automation

• Experience with Kubernetes and containerization technologies (Docker) is also required

• Additionally, you should have expertise in CI/CD pipelines, Helm charts, and monitoring solutions like Prometheus and Grafana

• 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

Responsibilities

• 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

• 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

• 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

• Implement containerization strategies using Docker or other relevant technologies

• 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

• Monitor and troubleshoot cloud infrastructure issues, particularly related to AI and data science applications

Full Description

About MedeAnalytics: Founded in 1993, MedeAnalytics is an innovation-focused company that has worked tirelessly to reimagine healthcare through the power of data.

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

We empower organizations to optimize their resource allocation, experience superior patient outcomes, and achieve population health management goals. With a deep understanding of the complex challenges facing the healthcare industry, we offer 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.

The Role: We are 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.

This role requires strong proficiency in AWS cloud services and tools, including Terraform and AWS CloudFormation for infrastructure automation.

Experience with Kubernetes and containerization technologies (Docker) is also required.

Additionally, you should have expertise in CI/CD pipelines, Helm charts, and monitoring solutions like Prometheus and Grafana.

If you thrive in a fast-paced, innovative environment and are committed to improving healthcare outcomes, we encourage you to apply.

Key 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.

• Implement containerization strategies using Docker or other relevant technologies.

• 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.

• Monitor and troubleshoot cloud infrastructure issues, particularly related 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.