Senior Automation and AI Developer

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EY Job Description

Job Title:AI Engineer - Agentic AI, Orchestration & Azure AI Foundry (Full-Stack, Azure-Native)

Job Rank (must note a single rank only for each job description):Supervising Associate

Function:EY Technology - Enterprise Technology

Scope (indicate either “global/cross-border” or “local/country”):Global

Sub Function:EY Technology | CBS Technology | Intelligent Automation

Reports to (Job Title):AI Engineering Lead

Job Summary:

The AI Engineer is responsible for building, deploying, and operating production-grade AI and Agentic AI solutions across the enterprise. This is a hands-on engineering role focused on implementing LLM-powered applications, orchestration and multi-agent workflows, and secure API-driven integrations using Azure AI Foundry and Azure-native services (compute, storage, messaging, data, security, and observability).

The role works across thefull AI lifecycle -from system design and development to production operations-ensuring solutions are secure, scalable, observable, and governed. The AI Engineer partners closely with Engineering Leads, architects, data science teams, and platform/security stakeholders to translate AI use cases into reliable, enterprise ready systems, not isolated proofs of concept.

A key expectation of the role is to embedevaluation, quality monitoring, and runtime security into AI systems, including the use ofLLM as a Judge patterns and alignment to agent lifecycle governance and runtime protection controls (e.g., Agent 365-aligned environments).

Essential Functions of the Job:

• Build Agentic AI Solutions using Azure AI Foundry (Core): Build and evolve AI applications and agents leveraging Azure AI Foundry-aligned capabilities used in the enterprise toolchain
• Develop Orchestration & Multi Agent Systems (Core): Implementorchestration layers to coordinate tools/agents across multi-step tasks, including multi-agent workflows for specialized sub-tasks and coordinated execution.
• Full Stack Engineering + API Development (Core): Build end-to-end AI experiences (UI where applicable), backend services, and integration layers. Design and implement RESTful APIs and microservices that expose AI/agent capabilities securely and reliably.
• Serverless & Asynchronous Processing with Azure Functions (Core): Build services using Azure Functions, including Durable Functions (or equivalent) for long-running and stateful orchestration patterns.
• Messaging / Queues for Workflow Reliability (Core): Use queue/event-driven patterns (e.g., messaging, pub/sub) to decouple services and improve reliability of multi-step AI pipelines and orchestration flows.
• Data Engineering Foundations: ADLS + Retrieval (Core): Work with ADLS -style data lake patterns for ingestion, storage, and processing to support AI workloads and grounding.
• Use Azure Cognitive Services / Azure AI Services Where Appropriate (Core: Leverage Azure Cognitive Services / Azure AI Services capabilities as part of end-to-end AI solutions.
• Vector Databases & Knowledge Stores (Core): Implement vector retrieval using enterprise options such as Azure AI Search and/or other vector DB patterns (e.g., Cosmos DB / PostgreSQL pgvector / Redis) based on operational needs.
• Continuous Evaluation using LLM-as-a-Judge (Core): Implement evaluation pipelines for LLM/agent outputs, including LLM-as-a-Judge patterns and structured scoring/assessment approaches where appropriate.
• Agent Lifecycle Governance with Agent 365 (A365) Awareness (Core): Build solutions that align with enterprise lifecycle management and governance patterns such as:
  • Agent registry / inventory expectations
  • Access controls and telemetry/observability requirements
  • Monitoring and operational controls at “agent scale
• Runtime Security / Runtime Protection (Core): Implement and support runtime protection expectations for agentic solutions, and participate in controls aligned to:
  • Runtime protection
  • Access controls (e.g., Entra ID patterns)
  • Threat detection and monitoring expectations

Analytical/Decision Making Responsibilities:

This role is critical to ensuring the enterprise’s AI ambition translates into real, reliable, and scalable systems-not just innovation theater. You will define how AI is built, shipped, and operated across the organization.

Knowledge and Skills Requirements:

• Core Software Engineering (Required)
  • Strong hands-on development in Python / C# / TypeScript/JavaScript (or similar).
  • Experience building API-driven services and integrating distributed systems. [Build agen...65 Copilot | SharePoint], [Agents in...65 Copilot | SharePoint]
  • Strong understanding of non-functional requirements: reliability, availability, scalability, performance, and cost.
• Azure & Platform Engineering
  • Hands on experience with Azure AI Foundry for delivering enterprise GenAI solutions in an enterprise context.
  • Experience building serverless and asynchronous workloads using Azure Functions (including Durable Functions or equivalent).
  • Experience using queues and event driven messaging for decoupled, reliable workflows.
  • Experience working with ADLS for data ingestion, storage, and processing.
  • Experience using Azure Cognitive Services / Azure AI Services as part of AI solutions.
• Azure Functions + Queues/Eventing (Required)
  • Experience with Azure Functions (including Durable Functions or equivalent orchestration patterns).
  • Experience implementing asynchronous patterns with queues/eventing for scale and reliability.
• Vector Databases (Required)
  • Hands-on experience withvector databases / vector search, including enterprise deployment patterns
• LLM-as-a-Judge Evaluation (Required)
  • Experience implementing evaluation approaches that include LLM-as-a-Judge (or equivalent automated evaluation patterns) for quality monitoring and continuous improvement.
• Agent 365 (A365) + Governance Alignment (Required)
  • Familiarity/experience working in environments using Agent 365 (A365) -style lifecycle management concepts (e.g., agent registry, governance, monitoring/observability, and access controls)
• Runtime Security / Runtime Protection (Required)
  • Experience delivering solutions with runtime protection expectations (runtime security controls, access controls, and monitoring alignment as defined by enterprise governance).

Nice to Have

• Experience integrating AI services into enterprise automation platforms (e.g., Power Platform, ServiceNow).
• Familiarity with Azure AI services and data platforms.

Detailed Responsibilities:

• AI & Agentic Solution Engineering
  • Build and evolve AI applications and agents using Azure AI Foundry and Azure native services.
  • Design and implementmulti agent workflows and orchestration logic for complex use cases.
  • Integrate AI capabilities into enterprise systems through secure APIs.
• Full Stack & Backend Development
  • Develop backend services, APIs, and supporting components for AI solutions.
  • Build full stack solutions where required, including UI integration for AI experiences.
  • Implement serverless components using Azure Functions for scalable, event driven execution.
• Data, Retrieval & Knowledge Systems
  • Design and implement data ingestion and storage pipelines using ADLS.
  • Build and operate vector based retrieval systems for AI grounding and search.
  • Maintain data freshness, indexing strategies, and retrieval performance.
• Evaluation, Monitoring & Quality
  • Implement evaluation pipelines for AI outputs, including LLM as a Judge-based scoring and analysis.
  • Instrument AI systems with logging, metrics, and telemetry to support observability and diagnostics.
  • Use evaluation insights to improve accuracy, consistency, and system behavior over time.
• Security, Governance & Operations
  • Build AI systems aligned to runtime security and governance expectations, including access controls and monitoring.
  • Operate and support AI solutions in production, including incident response and root cause analysis.
  • Continuously improve system reliability, performance, and cost efficiency.

Job Requirements:

Education:

• A degree in Computer Science / Engineering or a related discipline; or equivalent work experience

Experience:

• 10+ years in a Global IT environment working with multiple disciplines to deliver projects in line with customer needs
• 3+ Years Global delivery or transformation preferably in large scale infrastructure programs
• 3+ Years in a global operations environment

Certification Requirements:

• Python Certification
• Azure AI Services
• Azure Data Services

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