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AWS DevOps Engineer AI RAG/LLM : Remote

Lightning Minds Inc.United States🇺🇸United StatesPosted Sep 21, 2026

Why This Role Stands Out

This remote AWS DevOps Engineer role offers an exciting opportunity to build and optimize cutting-edge AI RAG/LLM solutions, leveraging your expertise in cloud-native pipelines and MLOps. You'll thrive here if you're a mid-senior engineer passionate about innovating in AI DevOps and driving scalable deployments within a dynamic, forward-thinking company. Apply now to shape the future of AI infrastructure!

Quick Overview

Seniority
Mid Senior
Work mode
Remote
Location
United States
Posted
Yesterday
DockerAPI GatewayAWSMLOpsAgileCloudFormationKubernetesLLMPythonTerraform

Job Description

AWS DevOps Engineer AI RAG/LLM
Location: Remote
Overview
We are seeking an experienced AWS DevOps Engineer with strong expertise in AI, RAG (Retrieval Augmented Generation), and Large Language Models (LLM). This role will focus on building and optimizing cloudnative pipelines, integrating AI/ML solutions, and ensuring scalable, secure, and efficient deployments across enterprise environments.
Key Responsibilities
  • Design and implement CI/CD pipelines for AI/ML workloads on AWS.
  • Deploy and manage RAGbased architectures integrating LLMs with enterprise data sources.
  • Automate infrastructure provisioning using Terraform/CloudFormation.
  • Optimize performance of LLM inference pipelines and retrieval systems.
  • Collaborate with data scientists and AI engineers to operationalize models.
  • Ensure compliance with security, monitoring, and logging standards.
  • Manage AWS services including EKS, Lambda, S3, Glue, SageMaker, API Gateway.
  • Drive innovation in AI DevOps practices for scalable deployments.
Required Skills & Experience
  • 6+ years in AWS DevOps engineering
  • Strong expertise in RAG architectures and LLM integration
  • Handson with Python, Docker, Kubernetes, Terraform, GitHub/BitBucket
  • Experience with AWS AI/ML services (SageMaker, Bedrock, Glue, Lambda, S3)
  • Knowledge of MLOps frameworks and AI pipeline optimization
  • Familiarity with data security, compliance, and monitoring tools
  • Strong background in Agile methodologies

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