Project Role Description : Build, configure, and test data center and AI infrastructure across compute, storage, networking, and platform layers. Translate architectural designs into scalable, secure, and high-performance production environments through reliable implementation and automation.
Must have skills : Automation Architecture
Good to have skills : NA
Minimum 7.5 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
Summary
As an Infrastructure Engineer, a typical day involves supporting the definition of requirements and contributing to the design and construction of data center technology components. The role includes active participation in testing activities to ensure the reliability and performance of infrastructure solutions. This position requires close collaboration with various teams to align technology implementations with organizational goals, while continuously monitoring and improving data center operations to maintain optimal functionality and efficiency.
Key Responsibilities
Design and build scalable agentic AI platforms supporting multi-step autonomous agents
Architect and implement Model Context Protocol (MCP) servers and client ecosystems
Develop agent adaptors for multiple LLMs, tools, and AI frameworks
Build Agent APIs (REST + gRPC) for lifecycle, streaming, and orchestration
Implement multi-agent execution patterns like ReAct and Plan-and-Execute
Enable memory, tool-calling, and context persistence for AI agents
Ensure security, observability, and reliability of agent workflows
Collaborate with ML, product, and platform teams on agentic system evolution
Required Skills And Qualifications
5+ years of software engineering with 2+ years in AI/LLM systems
Solid programming skills in Python and TypeScript / Node.js
Hands-on experience building production LLM or agentic systems
Solid understanding of LLM fundamentals (tokens, context, tools, promp