AWS DevOps Engineer with AI/ML Experience
Location: Alpharetta, GA (Onsite)
Job Type: Full-Time
Job Description
We are seeking an experienced AWS DevOps Engineer with AI/ML expertise to join our team in Alpharetta, GA. The ideal candidate will be responsible for designing, implementing, and managing cloud-native infrastructure, CI/CD pipelines, containerized applications, and AI/ML deployment platforms in AWS.
Required Skills
9+ years of experience in DevOps, Cloud Engineering, or Platform Engineering
Strong hands-on experience with AWS Cloud Services (EC2, EKS, ECS, Lambda, S3, IAM, VPC, CloudWatch)
Expertise in Terraform, CloudFormation, and Infrastructure as Code (IaC)
Experience with Docker, Kubernetes (EKS), Helm
Strong experience in CI/CD tools such as Jenkins, GitHub Actions, AWS CodePipeline, and CodeBuild
Proficiency in Python scripting and automation
Experience supporting AI/ML workloads using Amazon SageMaker, Amazon Bedrock, or similar AI platforms
Knowledge of MLOps/LLMOps concepts, model deployment, monitoring, and automation
Experience with monitoring tools such as CloudWatch, Prometheus, Grafana, and Splunk
Strong understanding of security best practices, IAM, Secrets Management, and DevSecOps
Experience with Git, Agile methodologies, and release management
Preferred Skills
Experience with Generative AI, RAG, Vector Databases, and AI Agents
Knowledge of MCP (Model Context Protocol)
Experience with ServiceNow integrations
AWS Certifications preferred
Responsibilities
Design and implement scalable AWS cloud infrastructure
Develop and maintain CI/CD pipelines for cloud and AI/ML applications
Deploy and manage containerized applications on Kubernetes/EKS
Automate infrastructure provisioning using Terraform and CloudFormation
Support MLOps/LLMOps pipelines for AI model deployment and monitoring
Implement observability, logging, and monitoring solutions
Collaborate with development, data engineering, and AI teams to deliver reliable cloud solutions
Ensure security, compliance, and operational excellence across environments
