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

Job description

Requisition ID:

1717211

Job Summary

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.

Key Responsibilities

• 1. 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.

• 2. 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.

• 3. 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.

• 4. 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.

• 5. 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.

• 6. 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, including architects, engineers, data scientists, process SMEs, security teams, and operations leads.

• Create reusable assets, architecture playbooks, demo journeys, and delivery patterns for ASEAN priority industries and service lines.

• Required Qualifications

• 10+ years of experience across AI/ML, solution architecture, platform engineering, data engineering, enterprise automation, or cloud-native application delivery.

• Hands-on experience delivering production AI/ML, GenAI, RAG, conversational assistant, or agentic automation solutions at enterprise scale.

• Strong proficiency in at least one major cloud stack: Microsoft Azure, AWS, or Google Cloud Platform, including AI services, data services, identity/security, networking, and deployment patterns.

• Practical software engineering capability in Python and one or more of TypeScript, Java, C#, or Go; strong understanding of APIs, microservices, integration design, and testing strategies.

• Experience with LLMOps/MLOps practices such as evaluation, prompt/version management, model registry, monitoring, CI/CD, guardrails, and release governance.

• Knowledge of enterprise workflow and automation patterns across HR, Finance, Procurement, Supply Chain, or shared-services operations.

• Strong understanding of security, privacy, responsible AI, data residency, access control, audit logging, and model risk considerations.

• Client-facing consulting experience, including structured problem solving, executive communication, workshop facilitation, solution shaping, and delivery leadership.

• Preferred Qualifications

• Experience with managed-services or shared-services operating models, including process transition, service catalogues, SLAs, runbooks, knowledge management, and continuous improvement.

• Hands-on experience with agent frameworks and orchestration tools such as LangGraph, Semantic Kernel, AutoGen, CrewAI, OpenAI Assistants, or equivalent frameworks.

• Experience with automation and workflow platforms such as Microsoft Power Platform, UiPath, Automation Anywhere, ServiceNow, Camunda, Temporal, Airflow, or cloud-native workflow services.

• Experience integrating with ERP, HCM, procurement, and service-management platforms such as SAP, Oracle, Workday, Coupa, Ariba, ServiceNow, Dynamics, and Microsoft 365.

• Famili