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About the Role

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.

Responsibilities

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.
Recognize when an agentic approach is not justified and recommend simpler, safer, or more cost-effective alternatives.
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.
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.
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 prioritize agentic AI opportunities based on value, feasibility, risk, and organizational 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.

Requirements

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.
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.
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.

Skills

Microsoft Agentic AI
Generative AI
AI Engineering
Solution Architecture
Client Consulting
Delivery Leadership
Python
APIs
Cloud-native design patterns
Large language models
Orchestration
Tool use
Grounding
Retrieval-augmented generation
Structured outputs
Context management
Evaluation
Multi-step workflows
Multi-agent workflows
State management
Memory management
Delegation
Human oversight
DevOps
CI/CD
Automated testing
Infrastructure as code
Observability
AI Security
Identity management
Privacy
Responsible AI
Governance
Azure AI Foundry
Azure OpenAI
Microsoft Agent Framework
Semantic Kernel
Copilot Studio
Power Platform
Azure AI Search
Azure API Management
Azure Functions
Durable Functions
Azure Container Apps
Azure Kubernetes Service
Azure messaging services
Azure integration services
Microsoft Entra ID
Azure Monitor
Application Insights
GitHub
Azure DevOps
Bicep
Terraform
Microsoft Fabric
Enterprise data platforms
Databricks
Snowflake
LangGraph
AutoGen

Location

Work Type

Permanent
Full-time

Experience Level

Senior Manager

About the Company

EY is building a better working world by creating new value for clients, people, society, and the planet, while building trust in capital markets.
Enabled by data, AI, and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy, and transactions.
Fueled by sector insights, a globally connected, multi-disciplinary network, and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.