Senior Manager - Microsoft Agentic AI Technical Lead, UKI

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• Senior Manager, Microsoft Agentic AI Technical Lead

The opportunity

We are looking for an experienced Microsoft Agentic AI Technical Lead at Senior Manager level to lead the design, engineering and delivery of enterprise-grade generative AI and agentic AI solutions for our clients.

This is a hands-on technical leadership role combining deep AI engineering expertise with solution architecture, client consulting and delivery leadership. You will translate complex business problems into secure, scalable and production-ready solutions, leading multidisciplinary teams from initial discovery and prototyping through to deployment and operation.

The role has a strong Microsoft focus, using technologies such as Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, Semantic Kernel and Copilot Studio. You will apply these technologies pragmatically, determining where agentic AI offers real value and where conventional applications, deterministic automation or human intervention provide a better solution.

You will be equally comfortable developing or reviewing working code, whiteboarding architectures with senior stakeholders, leading engineering teams and taking accountability for production outcomes.

Your key responsibilities

• Agentic AI architecture and engineering
• Lead the end-to-end technical design of enterprise generative AI and agentic AI solutions using Microsoft technologies.
• Translate business processes, user needs and control requirements into solution architectures, agent specifications, integration designs and engineering backlogs.
• Design single-agent and multi-agent solutions, selecting appropriate models, tools, orchestration patterns, grounding approaches, memory and human approval points.
• Make pragmatic platform decisions across Azure AI Foundry, Microsoft Agent Framework, Semantic Kernel, Copilot Studio, Azure OpenAI and custom Azure services.
• Design secure integrations between agents, enterprise applications, data platforms, knowledge sources and business workflows using APIs, events, messaging and appropriate tool protocols.
• Establish reusable reference architectures, engineering patterns and accelerators.
• Recognise when an agentic approach is not justified and recommend simpler, safer or more cost-effective alternatives.
• Hands-on technical leadership
• Lead the development of proofs of concept, minimum viable products, reusable accelerators and production AI components.
• Remain sufficiently hands-on to prototype critical components, review code and resolve complex technical issues.
• Guide the development of Python-based AI applications, APIs, microservices and cloud-native integrations.
• Define engineering standards covering source control, automated testing, CI/CD, infrastructure as code, secrets management and release management.
• Provide technical direction across orchestration, tool calling, retrieval-augmented generation, structured outputs, context management and asynchronous workflows.
• Ensure solutions are modular, testable and supportable rather than dependent on fragile prompts or opaque behaviour.
• Mentor AI engineers, software engineers and architects, helping develop the wider Microsoft AI & Data capability.
• Production engineering and responsible AI
• Lead the transition of AI solutions from experimentation into secure, scalable, observable and cost-effective production services.
• Define evaluation approaches covering functional quality, task completion, groundedness, safety, latency, reliability and cost.
• Establish automated evaluation, regression testing and release controls for changes to models, prompts, tools and orchestration.
• Design operational monitoring across agent workflows, including tracing, logging, performance, tool-call outcomes and cost.
• Define failure handling, retries, timeouts, fallbacks, human approval and exception-management patterns.
• Ensure solutions address identity, access control, data protection, content safety, auditability, responsible AI and regulatory requirements.
• Work with architecture, security, risk, legal and privacy stakeholders to establish proportionate controls for agents that access data or take actions.
• Define production support arrangements, operational runbooks and lifecycle-management processes.
• Client delivery and business development
• Act as the Agentic AI Technical Lead on significant client engagements, taking responsibility for technical quality and deliverability.
• Lead multidisciplinary teams of AI engineers, architects, software engineers, data engineers, business analysts and security specialists.
• Facilitate discovery, architecture and engineering workshops with senior business and technology stakeholders.
• Explain complex AI concepts, risks and design trade-offs clearly to technical and non-technical audiences.
• Manage technical scope, dependencies, risks and delivery priorities while maintaining focus on measurable business outcomes.
• Support opportunity shaping, demonstrations, RFP responses, estimates, proposals and statements of work.
• Help clients identify and prioritise agentic AI opportunities based on value, feasibility, risk and organisational readiness.
• Work with Microsoft account, engineering and specialist teams to validate technology choices and support joint client activity.
• Contribute to EY propositions, reference architectures, delivery methods, reusable intellectual property and recruitment.

Skills and attributes for success

We are looking for someone who combines deep engineering expertise with the consulting maturity to lead clients and delivery teams.

You should have

• Significant experience designing, building and operating enterprise AI, generative AI or agentic AI solutions.
• Strong hands-on software engineering experience using Python, APIs and cloud-native design patterns.
• Deep understanding of large language model applications, including orchestration, tool use, grounding, retrieval-augmented generation, structured outputs, context management and evaluation.
• Experience designing multi-step or multi-agent workflows and making informed decisions about state, memory, delegation and human oversight.
• Proven experience moving AI solutions from proof of concept into secure, reliable and supportable production environments.
• Practical experience of DevOps, CI/CD, automated testing, infrastructure as code and observability.
• Strong understanding of AI security, identity, privacy, responsible AI and governance.
• Strong solution architecture skills, balancing innovation with reliability, security, cost and operational complexity.
• Experience leading multidisciplinary engineering teams and working directly with senior client stakeholders.
• Experience supporting pre-sales, including solution shaping, demonstrations, estimation, proposals and RFP responses.
• Excellent communication skills and the ability to explain sophisticated technical concepts clearly.
• Microsoft technology experience
• Candidates should bring strong experience across several of the following:
• Azure AI Foundry and Azure OpenAI
• Microsoft Agent Framework and/or Semantic Kernel
• Copilot Studio and Power Platform

Azure AI Search

Azure API Management

• Azure Functions and Durable Functions
• Azure Container Apps and/or Azure Kubernetes Service
• Azure messaging and integration services

Microsoft Entra ID

• Azure Monitor and Application Insights
• GitHub and/or Azure DevOps
• Bicep or Terraform
• Microsoft Fabric and modern enterprise data platforms
• Experience with Databricks, Snowflake, LangGraph, AutoGen or comparable orchestration frameworks would be advantageous.

What we look for

A successful candidate will move naturally between conversations about business value, enterprise architecture, detailed engineering and credible delivery planning.

This is not a prompt-engineering role, a pure architecture role or a conventional delivery-management position. It requires a senior technologist who can design and build, lead engineers, engage credibly with clients and take responsibility for production outcomes.

Location and working arrangements

The position would be a permanent UK Senior Manager role, aligned to EY’s main UK offices, with travel to client sites as required.

Main changes: I removed repetition, consolidated overlapping production and engineering responsibilities, shortened the technology list, and retained the points most likely to differentiate a genuinely hands-on Agentic AI leader from a general AI architect or programme lead.

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