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 :
- Generative AI solution design and implementation
- LLM-based application architecture and integration
- Retrieval-Augmented Generation (RAG), vector search, and enterprise knowledge grounding
- Prompt engineering, prompt evaluation, and model behavior tuning
- AI workflow orchestration and tool/function calling patterns
- Enterprise API, data, and system integration for AI-powered applications
- AI model evaluation, quality measurement, monitoring, and optimization
- Azure AI Foundry, Azure OpenAI Service, and enterprise AI platform capabilities
- 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.
- Solid 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.

📌 Capgemini - Azure AI Leader (India)
🏢 Capgemini Technology Services
📍 India