Design, develop, test, and support AI Agents and Agentic AI workflows for DevOps and infrastructure operations.
Use frameworks such as LangGraph, LangChain, CrewAI, AutoGen, and Model Context Protocol (MCP).
Develop AI-powered solutions that automate infrastructure provisioning, deployment, monitoring, troubleshooting, maintenance, and remediation.
Develop software services, APIs, microservices, and workflows using Python and TypeScript/Nest.js.
Integrate AI solutions with cloud platforms, DevOps toolchains, monitoring systems, CMDBs, ticketing systems, APIs, and enterprise data sources.
Apply LLMs, prompt engineering, RAG, context engineering, Agentic RAG, and AI evaluation techniques to improve AI workflow accuracy and reliability.
Support AWS/Azure, Kubernetes, Docker, CI/CD, Infrastructure as Code, and GitOps environments.
Develop and maintain infrastructure automation using technologies such as Terraform, Bicep, Helm, and ArgoCD.
Develop monitoring and observability solutions to improve system availability, performance, and reliability.
Troubleshoot cloud/platform issues, outages, and operational problems and develop proactive AI-driven remediation recommendations.
Perform UAT, ORT, test planning, test case development, test execution, and production verification.
Develop technical documentation, operational guidelines, and support procedures.
Participate in Post-Outage Reviews (POR), Service Incident Reporting (SIR), and Root Cause Analysis (RCA).
Collaborate with developers, architects, QA teams, team leads, vendors, product teams, and customers.
Strong hands-on experience with Agentic AI frameworks, including one or more of:
LangGraph
LangChain
CrewAI
AutoGen
MCP
Strong Python development experience.
Experience with TypeScript and Nest.js preferred.
Strong understanding of Generative AI, LLMs, prompt engineering, RAG, and context engineering.
Experience developing and integrating AI Agents, tools, workflows, and enterprise APIs.
Experience with REST APIs, microservices, automation, and software development.
Strong understanding of Cloud and Infrastructure Engineering.
Experience with AWS and/or Azure.
Experience with Kubernetes and Docker.
Strong understanding of CI/CD and DevOps practices.
Hands-on Infrastructure as Code/GitOps experience with technologies such as:
Experience with monitoring, observability, troubleshooting, system reliability, and incident management.
Strong analytical and problem-solving skills.
Agentic RAG and vector databases such as Pinecone, Weaviate, Supabase, or Vectorize.
AI evaluation (Evals) and AI observability.
Experience with Java and/or Go.
JavaScript frameworks.
Data analytics, modeling, or data science experience.
Experience with Claude Code, Cursor, or Lovable.
Working experience with Figma, Windsurf, Replit, or Visual Studio Code.
AI-related certifications.
AWS or Azure certifications.
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