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EY Job Description
Job Title:
Integration Lead
Asst. Director
EY Technology | CBS Technology | Intelligent Automation Reports to (Job Title):
AI Engineering Lead
Job Summary:
The Integration Lead will own and evolve the enterprise integration layer that enables agentic AI systems to interoperate reliably with each other and with enterprise platforms. This role is central to scaling agentic AI beyond isolated solutions—ensuring consistent integration patterns, governed access to tools and data, and seamless agent-to-agent and agent-to-system communication.
- Senior integration engineer or architect with hands-on enterprise integration depth.
- Has integrated intelligent systems—not just applications.
- Thinks in protocols, contracts, and control boundaries, not point solutions.
- Pragmatic: able to support both custom-built and platform-based agent ecosystems.
Without a strong integration layer:
- Agents remain siloed and fragile
- Orchestrators become tightly coupled and hard to scale
- MCP and agent interoperability cannot be realized
- This role ensures agentic AI becomes a connected, enterprise-grade capability, not a collection of disconnected automations.
Essential Functions of the Job:
- Enterprise Integration Architecture for Agentic Systems
- Design and standardize integration patterns for agentic AI solutions, including:
- API-based integration
- Event-driven and asynchronous messaging patterns
- Hybrid deterministic + AI-driven flows
- Ensure integrations align with enterprise resilience, scalability, observability, and security standards, as reflected in the firm’s AI reference architecture.
2. Agent-to-Agent (A2A) Interoperability
- Enable agent-to-agent operability using standardized communication protocols and contracts.
- Implement and promote A2A communication patterns that allow agents to:
- Discover, collaborate, delegate tasks, and exchange context
- Operate across heterogeneous frameworks and vendors
- Align A2A implementations with emerging internal standards and protocol specifications
Orchestrator & SDK Integration
- Integrate and extend leading orchestration SDKs and frameworks used for agent coordination, routing, and workflow execution.
- Support integration between:
- Custom-built orchestrators
- Platform-based orchestrators (e.g., enterprise AI and automation platforms)
- Partner closely with orchestration and platform teams to ensure clean separation between control plane (orchestration) and integration plane (connectivity).
Model Context Protocol (MCP) Enablement
- Design and implement integrations with MCP servers to standardize how agents:
- Discover tools and services
- Invoke enterprise APIs and data sources
- Operate through a governed, observable contract layer
- Contribute to MCP adoption as a foundational integration mechanism across the agent ecosystem.
. Enterprise System & Platform Integration
Lead integration of agentic solutions with the enterprise application ecosystem, including:
- Core business systems, data platforms, automation tools, and third-party services
- Ensure consistent use of identity, access management, audit logging, and policy enforcement across integrations, aligned with AI governance expectations.
Integration Governance & Reusability
- Define reusable integration blueprints, templates, and accelerators for agentic solutions.
- Prevent fragmented, point-to-point agent integrations by enforcing:
- Common contracts
- Versioning strategies
- Dependency and lifecycle management
- Partner with AI governance and architecture forums to institutionalize integration standards.
Knowledge and Skills Requirements:
Core Integration Expertise
- Deep experience designing and implementing enterprise integration patterns (API, event-driven, asynchronous, microservices-based).
- Proven experience integrating complex distributed systems in large enterprises.
- Agentic & Orchestration Exposure
- Practical understanding of agent-to-agent communication concepts and interoperability challenges.
- Experience integrating with orchestration frameworks or SDKs used to coordinate AI workflows.
- Familiarity with emerging protocols such as Model Context Protocol (MCP) and A2A concepts.
Platform & Engineering Skills
- Strong API design skills (REST, eventing, contracts, schemas).
- Experience working with cloud-native services and enterprise integration tooling.
- Comfort operating across AI engineers, orchestration leads, security teams, and platform owners.
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:
- Microsoft Certified: Azure AI Engineer Associate (AI 102)
- Microsoft Certified: Azure Solutions Architect Expert (AZ 305)
- Enterprise architecture or distributed systems certifications (e.g., TOGAF).
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