AI DevOps Engineer (AWS)

About the role

Capco is seeking an experienced AI DevOps Engineer (AWS) to join our Technology & Engineering Practice. This is a senior individual contributor opportunity designed for those passionate about hands-on engineering, automation, cloud-native technologies, and solving complex platform challenges.

Responsibilities

• Design, build, and maintain scalable AWS cloud infrastructure supporting AI, Machine Learning, and Generative AI platforms.
• Develop Infrastructure as Code using Terraform (preferred), AWS CloudFormation, or equivalent technologies while building automated CI/CD pipelines for AI applications and services.
• Deploy, manage, and optimize containerized workloads using Docker and Kubernetes (Amazon EKS preferred), supporting AI model deployment and lifecycle management.
• Implement monitoring, logging, observability, security, and governance across cloud environments while optimizing reliability, scalability, performance, and cost efficiency.
• Collaborate with AI Engineers, Data Scientists, Data Engineers, and Solution Architects to continuously improve platform engineering, automation, and operational excellence.

Requirements

• 5–7 years of experience in DevOps, Cloud Engineering, Platform Engineering, or Site Reliability Engineering, supported by a Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
• Strong hands-on experience with AWS cloud services, Infrastructure as Code (Terraform preferred), CI/CD tooling, Docker, Kubernetes, Linux administration, and cloud networking fundamentals.
• Experience supporting AI, Machine Learning, data platforms, or MLOps environments, with strong scripting skills in Python and/or Bash.
• Experience implementing monitoring, observability, IAM, secrets management, cloud security, governance, and operational best practices using tools such as CloudWatch, Prometheus, Grafana, Datadog, or Splunk.
• Strong troubleshooting, communication, collaboration, and stakeholder engagement skills with the ability to work effectively across cross-functional engineering teams.

Bonus Points For

• Experience with Microsoft Azure and/or Google Cloud Platform.
• Experience supporting Generative AI, Large Language Models (LLMs), MLOps, MLflow, SageMaker, Kubeflow, or similar technologies.
• Familiarity with vector databases, AI inference platforms, or modern model deployment architectures.
• Experience working within Financial Services or other highly regulated industries.

Pay

$XX.XX - $YY.YY per hour

Schedule

Hybrid schedule, with flexibility to work from home or office based on project needs.