AI/ML Solution Architect - Agentic Automation (Managed Services) - Consulting

Responsibilities

• Solution Architecture for Agentic Managed Services
• Design end-to-end agentic automation architectures for managed-service processes such as hire-to-retire, record-to-report, procure-to-pay, source-to-contract, order-to-cash, service desk, knowledge operations, and compliance operations.
• Translate business outcomes into solution blueprints, capability maps, technical roadmaps, non-functional requirements, success measures, and implementation backlogs.
• Define reusable reference architectures for AI agents, RAG, workflow orchestration, human-in-the-loop review, exception handling, audit trails, and enterprise knowledge management.
• Balance build, buy, and partner options across hyperscalers, AI platforms, automation tools, enterprise SaaS, and EY assets.
• Hands-on Engineering and Prototyping
• Build working PoCs, MVPs, accelerators, and production components using Python, TypeScript, APIs, microservices, event-driven patterns, and cloud-native services.
• Implement RAG pipelines, tool-calling agents, orchestration graphs, evaluation harnesses, prompt and policy controls, and observability dashboards.
• Develop integrations with enterprise systems such as SAP, Oracle, Workday, ServiceNow, Coupa, Ariba, Microsoft 365, Dynamics, Salesforce, and document management platforms.
• Guide engineering teams on coding standards, CI/CD, test automation, infrastructure as code, release management, and operational runbooks.
• Cloud and Platform Architecture
• Architect secure, scalable solutions on one or more hyperscaler stacks: Microsoft Azure, AWS, or Google Cloud Platform.
• Use native AI, data, integration, identity, security, and observability capabilities, including Azure AI Foundry/Azure OpenAI, AWS Bedrock/SageMaker, Google Vertex AI/Gemini, cloud data platforms, serverless services, container platforms, and managed Kubernetes.
• Design hybrid and regulated deployment patterns covering private networking, identity federation, secrets management, encryption, data residency, model risk, and compliant logging.
• Define cost-control mechanisms including model routing, caching, batching, token governance, scaling policies, FinOps dashboards, and usage analytics.
• Agentic Automation and Process Transformation
• Design agentic patterns such as planning, routing, delegation, tool use, memory, reflection, approval workflows, and multi-agent collaboration for enterprise operations.
• Apply process-mining, workflow, and task-automation concepts to redesign managed-service processes before automating them.
• Create human-in-the-loop controls for sensitive steps such as payment approvals, employee actions, vendor changes, reconciliations, policy exceptions, and regulatory submissions.
• Define measurable operating outcomes, including automation rate, exception rate, first-time-right quality, handling time, SLA compliance, leakage reduction, and cost-to-serve improvement.
• Reliability, Security, Risk, and Governance
• Establish LLMOps and MLOps practices for model/prompt versioning, evaluation, guardrails, monitoring, rollback, incident response, and quality assurance.
• Embed AI governance controls for responsible AI, data privacy, access control, auditability, explainability, model risk, and regulatory compliance.
• Implement production observability across logs, traces, metrics, user feedback, groundedness, hallucination risk, tool execution, cost per request, and service-level performance.
• Lead design reviews, threat modeling, architecture assurance, performance tuning, and post-implementation optimization.
• Client Advisory, Pursuits, and Delivery Leadership
• Partner with client executives, managed-service leaders, function owners, CIO/CTO teams, and ecosystem partners to shape AI-led transformation opportunities.
• Lead discovery workshops, value framing, solution estimation, PoVs/PoCs, business cases, acceptance criteria, and transition plans from prototype to managed operations.
• Coach cross-functional teams across EY, client, and partner organizations, includin

Additional Information

EY Consulting is hiring an AI/ML Solution Architect to design, build, and industrialize agentic automation solutions for Managed Services across HR, Finance, Procurement, Supply Chain, Risk, Tax, and other enterprise functions. The role combines hands-on engineering with solution architecture, bringing together Agentic AI, GenAI, workflow orchestration, enterprise integration, cloud-native platforms, and managed-service operating models to improve productivity, quality, cycle time, compliance, and user experience. The role is a practical technologist who can whiteboard an architecture with executives, prototype an agentic workflow with engineers, and guide teams through secure, reliable, and cost-efficient production delivery on Microsoft Azure, AWS, or Google Cloud Platform. The role requires strong experience with LLMs, RAG, agents, integration patterns, automation platforms, MLOps/LLMOps, and enterprise-grade governance.

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