Capgemini

Agentic AI Architect

Posted On

Feb. 25, 2026

Location

Noida, Uttar Pradesh

Experience Level

Experienced Professionals

Job Type

Permanent

Apply By

March 18, 2026

Job Description

We seek a visionary and technically strong Agentic AI Leader to architect and scale enterprise wide agentic AI solutions across our businesses. You will define the strategy, lead multi agent system delivery, and enforce responsible AI governance to transform complex workflows with measurable ROI and regulated grade safety.

We are looking for a strong Agentic AI Leader to drive enterprise-wide AI transformation initiatives with a deep focus on Agentic AI, Generative AI (GenAI), and intelligent automation. This role demands expertise in multi-agent orchestration, LLM-based architectures, and enterprise AI strategies.

Key Responsibilities:

• Define the Agentic AI strategy and roadmap, aligned to enterprise digital transformation, regulatory priorities, and growth objectives.

• Own end to end delivery of agentic AI initiatives from discovery and feasibility through architecture, build, production deployment, and value realization.

• Identify high impact automation opportunities where intelligent agents can orchestrate complex, multi step workflows.

• Lead and direct cross functional squads (AI/ML engineers, data scientists, domain SMEs, platform/IT, risk/compliance) to deliver robust, scalable Solution on time and on budget.

• Adhere to organizational best practices for agent design by embedding advanced planning and reasoning capabilities, secure and compliant tool integration, robust memory and state management, and optimized retrieval strategies for performance and scalability.

• Collaborate with Product, Technology, and Platform teams to secure infrastructure, tooling, and data required to scale agent-based solutions.

• Foster a culture of innovation, and continuous learning playbooks, and re usable assets/accelerators.

• Promote engineering excellence (coding standards, MLOps/AIOps, testing, observability) and domain literacy across delivery teams.

• Strong business acumen with the ability to build business cases & ROI driven proposals.

• Stakeholder influence and executive storytelling; ability to lead geographically dispersed, multi disciplinary teams.

• Knowledge of industry best practices in automation, governance, process improvement, and change management for enterprise adoption.

Our Ideal Candidate

Experience: 18+ years of progressive leadership in technology, strategic consulting, or complex program delivery; 5+ years driving GenAI/agentic AI programs at enterprise scale.
Education: Bachelor’s or Master’s in Computer Science or Ph.D., Engineering, Business Administration, or related quantitative field.
Agentic AI Expertise: Demonstrable end‑to‑end delivery of solutions using LLM orchestration frameworks, planning & reasoning architectures, memory/state management, and secure tool‑use by agents.
Deep expertise in modern AI/ML technology stacks, proficiency with leading cloud platforms (Azure, GCP), strong understanding of data governance principles, and hands-on experience in API integration with core enterprise systems
Proven ability to lead large, geographically dispersed teams; exceptional communication and executive presence, able to translate technical detail to business impact.
Agentic orchestration & frameworks: LangGraph, Semantic Kernel, Agent Framework, CrewAI (or equivalent), and Model Context Protocol (MCP) for cross‑agent interoperability.
Multimodal Agents: Architect and deliver solutions leveraging Azure AI capabilities, including Azure AI Foundry, Azure OpenAI Service, Cognitive Services (Vision, Speech, Language), Document Intelligence, and Conversational AI enabling agents to process documents, conversational interfaces (chat/voice), tabular and transactional data, knowledge of OCR/NLP pipelines and speech recognition frameworks for enterprise grade.
Cloud & AI services: Azure AI Foundry/Agent Service, Copilot Studio, Azure OpenAI (or equivalents AWS Sagemaker, GCP Vertex AI); strong MLOps/AIOps patterns and CI/CD for ML.

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