Description

Design and optimize AI-enabled CI/CD pipelines and Model Context Protocol (MCP) services to connect enterprise LLMs with engineering tools and cloud infrastructure.

This role is on-site.

Responsibilities

• Develop and deploy MCP clients and servers using Python, TypeScript, JavaScript, or Node.js to expose infrastructure data to AI agents.
• Build and maintain enterprise CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, CircleCI, or ArgoCD.
• Implement AI-driven ChatOps and intelligent remediation workflows for incident detection, root cause analysis, and operational troubleshooting.
• Deploy containerized applications using Kubernetes and Docker across AWS, Azure, or Google Cloud.
• Develop secure Infrastructure-as-Code automation using Terraform, OpenTofu, Pulumi, Terragrunt, or CloudFormation.

Required Skills

• 7+ years of experience in DevOps, Platform Engineering, SRE, or Infrastructure Automation.
• 4+ years designing and supporting enterprise CI/CD pipelines.
• 3+ years of cloud engineering experience in AWS, Azure, or Google Cloud (AWS preferred).
• Strong experience deploying and managing Kubernetes and Docker in production environments.
• Hands-on experience with Infrastructure as Code using Terraform, OpenTofu, Pulumi, Terragrunt, or CloudFormation.
• Strong programming skills in Python, TypeScript, JavaScript, Bash, Go, or similar languages.
• Experience integrating enterprise LLM platforms such as OpenAI, Anthropic, or equivalent AI services.
• Experience with AI orchestration frameworks such as LangChain, CrewAI, LlamaIndex, or similar technologies.
• Experience implementing DevSecOps practices including SAST, DAST, and secrets management.

Preferred Skills

• Experience building AI-assisted infrastructure provisioning and deployment workflows.
• Experience implementing autonomous or AI-assisted incident response and operational remediation.
• Experience with MLOps platforms including MLflow, Amazon SageMaker, Vertex AI, or Azure ML.

Key Skills

Education

Any Graduate