AWS DevOps Engineer – AI/ML & MLOps

Infosys Pontoon•Tampa, FL

3d•Onsite

About The Position

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.

Requirements

AWS cloud environments
Terraform
Infrastructure as Code (IaC)
CI/CD automation
AI/ML platform deployment
containerization
cloud-native technologies
MLOps
automation
GitHub Actions, Jenkins, GitLab CI/CD, or AWS CodePipeline
GitOps and DevSecOps best practices
Docker
Amazon EKS
Helm charts
CloudWatch, Prometheus, Grafana, and ELK Stack
IAM policies, security controls, secrets management, and vulnerability scanning

Nice To Haves

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

Responsibilities

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.
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.
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.
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.
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.