Description

Primary Skills: AWS, DevOps, CI/CD, Python, AI Agents, MCP, LLM Integration

Job Summary

We are looking for an AI Engineer with strong hands-on expertise in AWS Cloud and DevOps, along with practical experience in developing, integrating, and deploying AI agents in production environments. The ideal candidate will have a strong DevOps foundation and experience applying AI engineering capabilities to build scalable, secure, and automated AI solutions.

Key Responsibilities

•     Design, develop, integrate, and deploy AI agents and LLM-powered applications on AWS.

•     Build and maintain CI/CD pipelines for AI applications, automated testing, and production releases.

•     Provision and manage AWS infrastructure using Terraform, CloudFormation, or other Infrastructure as Code tools.

•     Develop AI agent workflows involving tool calling, API integrations, orchestration, and multi-step execution.

•     Implement and integrate Model Context Protocol (MCP) servers and tools to connect AI agents with enterprise systems and data sources.

•     Containerize and deploy AI applications using Docker, Kubernetes, Amazon EKS, ECS, or equivalent platforms.

•     Automate infrastructure provisioning, configuration management, application deployment, and rollback processes.

•     Implement monitoring, logging, observability, security controls, and performance optimization for AI workloads.

•     Apply DevSecOps practices, including IAM, secrets management, vulnerability scanning, and secure deployment pipelines.

•     Troubleshoot infrastructure, application, and deployment issues across development, testing, and production environments.

Required Skills

AWS & DevOps

•     Strong hands-on experience with AWS services such as EC2, S3, IAM, VPC, and CloudWatch.

•     Experience with CI/CD tools such as Jenkins, GitHub Actions, or GitLab CI/CD.

•     Proficiency in Docker, Kubernetes, Amazon EKS/ECS, and containerized application deployment.

•     Experience with Terraform, CloudFormation, or equivalent IaC tools.

•     Strong Python and/or Bash scripting skills, Linux administration, Git, networking, and cloud security fundamentals.

•     Experience with monitoring and observability tools such as CloudWatch, Prometheus, or Grafana.

AI Engineering & Agent Development

•     Practical experience building and integrating AI agents and LLM-powered applications.

•     Knowledge of agent orchestration, function/tool calling, API integration, and workflow automation.

•     Experience with LangChain, LangGraph, or equivalent agent development frameworks.

•     Understanding or hands-on experience with MCP servers, MCP clients, and custom MCP tool integration.

•     Experience integrating LLMs through APIs or managed cloud AI services; Amazon Bedrock experience is preferred.

•     Knowledge of prompt engineering, RAG, embeddings, and vector databases is desirable.

•     Ability to deploy, monitor, troubleshoot, and maintain AI applications in production.

Preferred Qualifications

•     Experience with MLOps/LLMOps, automated testing, release management, and deployment rollback strategies.

•     Knowledge of application reliability, scalability, performance optimization, and cloud cost management.

•     Experience integrating AI agents with enterprise applications, APIs, databases, and operational tools.

•     Familiarity with Agile/Scrum delivery practices.

Core Skills: AWS | DevOps | CI/CD | Python | Terraform | Docker | Kubernetes | AI Agents | LLM Integration | MCP | Production Deployment | Monitoring | DevSecOps

Key Skills

Education

Any Graduate