Capgemini - Azure AI Leader

Role :

We are looking for a seasoned AI technology leader to drive the strategy, architecture, governance, and hands-on delivery of enterprise-grade Generative AI and Agentic AI solutions. This role requires deep expertise in AI solution architecture, large language model integration, multi-agent orchestration, responsible AI adoption, AI security, observability, regulatory alignment, stakeholder engagement, and production-scale AI delivery across complex enterprise environments.

Experience Required :

- 12+ years of overall experience in the IT industry, with a proven track record of leading technical teams and delivering enterprise solutions.

- 6+ years of relevant hands-on experience in AI, machine learning, Generative AI, cloud-native AI platforms, or enterprise AI solution delivery, including :

1. Generative AI solution design and implementation

2. LLM-based application architecture and integration

3. Retrieval-Augmented Generation (RAG), vector search, and enterprise knowledge grounding

4. Prompt engineering, prompt evaluation, and model behavior tuning

5. AI workflow orchestration and tool/function calling patterns

6. Enterprise API, data, and system integration for AI-powered applications

7. AI model evaluation, quality measurement, monitoring, and optimization

8. Azure AI Foundry, Azure OpenAI Service, and enterprise AI platform capabilities

9. Microsoft's current agent architecture capabilities, including multi-agent orchestration, connected/child agents, agent-to-agent A2A patterns, Model Context Protocol (MCP), and Azure AI Foundry Agent Service for code-first agents.

- Strong understanding of enterprise AI architecture, including AI platform strategy, model selection, data grounding, integration patterns, security, governance, LLMOps, deployment lifecycle, scalability, and production operations.

- 2+ years of hands-on experience in customized Agentic AI development, including designing and building autonomous/semi-autonomous AI agents, orchestrating multi-agent workflows, and integrating LLM-based reasoning into business applications.

- Experience defining production disciplines for agentic AI, including governance, observability, agent evaluation, AI security, compliance, and cost/capacity modelling for scaled deployments.

- Demonstrated ability to align AI initiatives with responsible AI principles, enterprise governance policies, and applicable regulatory requirements across the full AI lifecycle.

Key Responsibilities :

- Lead the end-to-end architecture, design, and delivery of enterprise-grade Generative AI and Agentic AI solutions across business-critical use cases.

- Provide hands-on technical leadership by designing and building AI applications, agentic workflows, RAG-based solutions, model integrations, API-enabled AI capabilities, and reusable enterprise AI components.

- Architect and implement Copilot Studio and Azure AI Foundry based solutions, including custom agent development, prompt engineering, orchestration of agentic workflows, and Azure AI Foundry Agent Service based code-first agent patterns.

- Design and govern multi-agent architectures using connected/child agents, agent-to-agent A2A collaboration patterns, and Model Context Protocol (MCP) for standardized tool, workflow, and enterprise data integration.

- Ensure the ethical and responsible use of AI technologies by embedding responsible AI principles, risk discovery, protection controls, governance checkpoints, and audit-ready practices into solution delivery.

- Define and enforce enterprise AI architecture standards, including model lifecycle management, data grounding, security, deployment patterns, LLMOps, governance, scalability, and operational best practices.

- Own agent governance and observability using Microsoft Agent 365 concepts, including agent registry, lifecycle governance, security posture, access controls, telemetry, usage insights, credit/cost controls, and operational dashboards.

- Define agent evaluation frameworks, including eval harnesses, outcome-based quality metrics, red-teaming, prompt-injection testing, safety checks, and production-readiness gates before rollout.

- Provide leadership in aligning AI programs with enterprise security, compliance, data governance, transparency, accountability, and regulatory expectations for responsible enterprise AI adoption.

- Design and build custom AI agents capable of autonomous decision making, task execution, and integration with enterprise systems via APIs.

- Collaborate with business stakeholders to identify opportunities for automation and AI-driven transformation, translating requirements into scalable technical solutions.

- Mentor and guide a team of developers and consultants, conducting code/solution reviews and ensuring adherence to best practices.

- Facilitate workshops, design reviews, and enablement sessions to upskill teams on Azure AI Foundry, agent orchestration, AI governance, observability, responsible AI practices, and production readiness.

- Own technical delivery across the project lifecycle, from solution design and proof-of-concepts to deployment, monitoring, and optimization.

- Stay current with evolving Generative AI, Azure AI Foundry, Agentic AI, multi-agent orchestration, AI security, and responsible AI capabilities, and proactively recommend adoption of relevant features and architecture patterns.

- Partner with cross-functional teams (data engineering, security, cloud infrastructure) to ensure robust, secure, and compliant AI powered solutions.

- Strengthen AI security and compliance by applying Microsoft Purview for DLP and data governance, Microsoft Sentinel for security monitoring, delegated-identity patterns such as OAuth On-Behalf-Of (OBO), and data-residency / EU Data Boundary considerations for regulated clients.

- Develop agentic AI cost and capacity models covering Copilot credits, Azure consumption, runtime scaling, usage forecasting, and total cost of ownership for enterprise-scale deployments.

Required Skills :

- Deep expertise in Generative AI solution architecture, LLM-based application design, AI platform engineering, and enterprise-scale AI implementation.

- Strong grasp of enterprise data grounding, RAG architecture, vector databases, knowledge indexing, API integration, and secure enterprise system connectivity.

- Hands-on experience building conversational AI, task-oriented AI agents, autonomous/semi-autonomous workflows, and enterprise copilots or assistants.

- Practical experience with Microsoft Azure AI Foundry for building, deploying, observing, governing, and managing enterprise-grade AI agents and agentic AI solutions.

- Hands-on understanding of Azure AI Foundry Agent Service for building, deploying, and scaling secure code-first agents.

- Demonstrated experience building custom Agentic AI solutions, agent orchestration, tool/function calling, memory management, and multi-agent collaboration.

- Working knowledge of multi-agent orchestration patterns, connected/child agents, agent-to-agent A2A collaboration, MCP-based tool/data integration, and enterprise agent interoperability.

- Strong capability in agent governance, observability, evaluation, red-teaming, prompt-injection mitigation, and production-readiness assessment.

- Strong understanding of responsible AI frameworks, ethical AI adoption, compliance-by-design, model risk controls, transparency, accountability, and human oversight considerations for enterprise AI systems.

- Knowledge of Microsoft Agent 365, Microsoft Purview, Microsoft Sentinel, Microsoft Entra identity patterns, OAuth OBO flows, data residency, EU Data Boundary, and AI compliance controls.

- Ability to estimate and optimize agentic AI run costs, including Copilot credit consumption, Azure usage, capacity planning, and scale economics.

- Excellent stakeholder management, communication, and team leadership skills.

- Experience with Azure OpenAI Service, Semantic Kernel, LangChain, LangGraph, and similar Gen AI/agentic frameworks.

Good to Have :

- Microsoft certifications in Azure AI, Azure AI Engineer, Generative AI, cloud AI architecture, data engineering, security, or related AI technologies.

- Exposure to enterprise integration platforms, DevOps/MLOps/LLMOps practices, CI/CD pipelines, model deployment automation, and AI solution release governance.

- Exposure to enterprise-scale agent management, governance control planes, AI security operations, and production rollout frameworks for regulated industries.

- Prior experience in a pre-sales, solutioning, or architect capacity.

- Ability to advocate for responsible AI adoption across business, technology, security, data, and compliance stakeholders.