AI Architect - Agentic AI

Job Description

AI Architect - Agentic AI

We are seeking a hands-on AI Architect to design and deliver advanced Agentic AI and automation solutions for enterprise use cases.

The role focuses on building intelligent, autonomous systems that can reason, interact, and execute complex workflows by combining large language models, multi-agent systems, and enterprise integrations.

You will lead a technical team and work closely with global AI experts to drive innovation, efficiency, and scalability across business operations.

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What You Will Do

• Design and implement Agentic AI systems with capabilities such as:

Autonomous decision-making

• Multi-agent collaboration (A2A)

• Tool usage and workflow orchestration

• Develop and deploy AI solutions leveraging:

Large Language Models (LLMs)

• Retrieval-Augmented Generation (RAG)

• Knowledge-driven systems and intelligent workflows

• Build intelligent agents that:

Interact with enterprise applications

• Automate complex business processes

• Execute end-to-end workflows

• Define architecture patterns for:

Agent orchestration

• Context and memory management

• Multi-step reasoning and execution pipelines

• Lead and mentor a team of AI engineers, ensuring high-quality delivery

• Collaborate with global AI teams to align on architectures, frameworks, and best practices

• Drive rapid prototyping and deployment of AI solutions for real-world use cases

• Ensure solutions meet enterprise standards for performance, scalability, and security

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What You Need to Be Successful

• Strong experience in AI/ML and Generative AI systems

• Hands-on expertise in:

Python and modern AI/ML frameworks

• LLM-based application development

• API and microservices-based architectures

• Experience with:

Agentic AI frameworks and multi-agent systems

• RAG pipelines and vector databases

• Enterprise system integration

• Strong problem-solving ability with ownership of end-to-end AI solution delivery

Experience or Familiarity with:

Model Context Protocol (MCP)

• Agent-to-Agent (A2A) communication patterns

• Agent orchestration frameworks (Lang Graph-style models)

• Planning agents and reasoning-based systems

• Context-aware and memory-enabled agents

• AI governance, safety, and guardrails

• Prompt and response control mechanisms

• Agent security (access control, tool permissions)

• Exposure to:

AI system observability and evaluation

• Hybrid or on-premise AI deployments

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What We Offer

• Opportunity to build next-generation autonomous AI systems

• Work on high-impact enterprise use cases

• Exposure to cutting-edge Agentic AI technologies

• Collaborative environment with strong technical leadership