Agentic AI Solution Architect

HCLTech; Dadri; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

HCL Technologies Limited is seeking an Agentic AI Solution Architect to design and deliver secure, scalable AI agent solutions for large enterprises. You will architect single-agent and multi-agent systems, plan tasks, orchestrate workflows, and ensure production readiness across cloud-native stacks.

The role emphasizes practical hands-on Python development, LLM integration, and secure, observable deployments with CI/CD and DevSecOps practices.

Qualifications

• Hands-on Python development and enterprise solution architecture.
• Practical experience building GenAI and Agentic AI applications.
• Multi-agent architecture and agent orchestration frameworks.
• LLMs, RAG, embeddings, vector databases, and context management.
• MCP, REST APIs, tool calling, and enterprise integration.
• Docker, Kubernetes, CI/CD, DevSecOps, and secure application engineering.
• AI evaluation, observability, and production-readiness practices.

Responsibilities

• Architect single-agent and multi-agent systems for enterprise use cases.
• Design agent planning, task decomposition, orchestration, state management, memory, and tool execution.
• Build AI agent applications using LangGraph, AutoGen, CrewAI, Semantic Kernel, or comparable frameworks.
• Implement LLM integration, RAG, vector search, embeddings, and context engineering.
• Develop MCP servers, tool/function-calling interfaces, and secure enterprise app integrations.
• Design cloud-native AI applications using Python, APIs, microservices, Docker, and Kubernetes.
• Establish agent security, guardrails, human approval, observability, evaluation, and failure recovery.
• Implement CI/CD, DevSecOps, LLMOps, and production monitoring.
• Optimize AI applications for reliability, latency, scalability, and cost.

Skills

Python development

Enterprise architecture

GenAI & Agentic AI

Multi-agent architecture

Agent orchestration

LLMs & RAG

Embeddings & vector databases

Context management

REST APIs

Tool calling

Enterprise integration

Docker

Kubernetes

CI/CD

DevSecOps

Observability

Production readiness

Security engineering

Tools

LangGraph

AutoGen

CrewAI

Semantic Kernel

MCP servers

Docker

Kubernetes

Job description

Function: Generative AI | Agentic AI | Enterprise AI Engineering

Build Enterprise AI Agents That Work in Production

HCLTech is looking for a hands-on Agentic AI Solution Architect to design and deliver intelligent, secure, scalable AI agent solutions for complex enterprise environments.

We're seeking an architect who goes beyond GenAI demonstrations and has practical experience engineering multi-agent systems, integrating enterprise applications, and deploying AI workloads into production.

What You'll Do

• Architect single-agent and multi-agent systems for enterprise use cases.
• Design agent planning, task decomposition, orchestration, state management, memory, and tool execution.
• Build AI agent applications using LangGraph, AutoGen, CrewAI, Semantic Kernel, or comparable frameworks.
• Implement LLM integration, RAG, vector search, embeddings, and context engineering.
• Develop MCP servers, tool/function-calling interfaces, and secure enterprise application integrations.
• Design cloud-native AI applications using Python, APIs, microservices, Docker, and Kubernetes.
• Establish agent security, guardrails, human approval, observability, evaluation, and failure recovery.
• Implement CI/CD, DevSecOps, LLMOps, and production monitoring.
• Optimize AI applications for reliability, latency, scalability, and cost.
• Develop reusable architecture patterns and guide engineering teams through production deployment.

What We're Looking For

Mandatory expertise:

• Hands-on Python development and enterprise solution architecture.
• Practical experience building GenAI and Agentic AI applications.
• Multi-agent architecture and agent orchestration frameworks.
• LLMs, RAG, embeddings, vector databases, and context management.
• MCP, REST APIs, tool calling, and enterprise integration.
• Docker, Kubernetes, CI/CD, DevSecOps, and secure application engineering.
• AI evaluation, observability, and production-readiness practices.

Preferred:

Azure AI Foundry, Amazon Bedrock, Vertex AI, GraphRAG, Neo4j, LangSmith, RAGAS, OpenTelemetry, and infrastructure as code.

Why This Role?

You'll architect AI systems that can reason, retrieve context, interact with enterprise tools, and execute governed workflows at scale.

If you've taken Agentic AI solutions from architecture to production, we want to hear from you.

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