AWS DevOps Engineer AI/ML & MLOps

The ideal candidate will be responsible for building, automating, and managing scalable cloud infrastructure on AWS while enabling AI/ML workloads through robust DevOps practices. Nice-to-Have Generative AI deployment experience using Amazon Bedrock, OpenAI, Anthropic, or Hugging Face models.

Job title: AWS DevOps Engineer AI/ML & MLOps
Work Location: Tampa, FL

Job Description:
We are looking for an experienced AWS DevOps Engineer with strong expertise in Terraform, CI/CD automation, and AI/ML platform deployment. The ideal candidate will be responsible for building, automating, and managing scalable cloud infrastructure on AWS while enabling AI/ML workloads through robust DevOps practices. This role requires hands-on experience in Infrastructure as Code (IaC), containerization, cloud-native technologies, MLOps, and automation.

Key Responsibilities
Cloud Infrastructure & Automation

• Design, deploy, and manage highly available and secure AWS cloud environments.
• Develop and maintain Infrastructure as Code (IaC) using Terraform.
• Automate cloud provisioning, configuration management, and environment setup.
• Implement cloud governance, security, compliance, and cost optimization strategies.

DevOps & CI/CD

• Design and manage CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI/CD, or AWS CodePipeline.
• Automate application deployments across development, testing, and production environments.
• Implement GitOps and DevSecOps best practices.
• Manage source control repositories and branching strategies.

AI/ML & MLOps

• Deploy, automate, and manage AI/ML solutions on AWS.
• Support ML lifecycle management, including model training, validation, deployment, and monitoring.
• Work with Amazon SageMaker for model development and deployment.
• Implement MLOps pipelines for continuous model integration and delivery.
• Collaborate with Data Scientists and AI Engineers to operationalize machine learning models.

Containerization & Orchestration

• Build and manage containerized workloads using Docker.
• Deploy and manage Kubernetes clusters using Amazon EKS.
• Implement Helm charts and Kubernetes best practices for scalable deployments.
• Monitoring & Security
• Configure monitoring, logging, and alerting using CloudWatch, Prometheus, Grafana, and ELK Stack.
• Implement IAM policies, security controls, secrets management, and vulnerability scanning.
• Monitor infrastructure health and optimize system performance.

Nice-to-Have

• Generative AI deployment experience using Amazon Bedrock, OpenAI, Anthropic, or Hugging Face models.
• Experience with LLM deployment, vector databases, and RAG architectures.
• Knowledge of LangChain, AI Agents, and AI workflow automation.
• Exposure to Data Engineering tools such as Glue, Athena, EMR, or Redshift.
• Experience implementing AI governance and model security frameworks.