AI/ML Solution Architect - Agentic Automation (Managed Services) - Consulting
Ernst and Young1 day ago
Singapore, SingaporeStaff+
Responsibilities
- Design end-to-end agentic automation architectures, reference architectures, technical roadmaps, and implementation backlogs for managed-service processes.
- Build 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.
- Architect secure, scalable, hybrid, and regulated solutions on Microsoft Azure, AWS, or Google Cloud Platform.
- Integrate enterprise platforms including SAP, Oracle, Workday, ServiceNow, Coupa, Ariba, Microsoft 365, Dynamics, Salesforce, and document-management systems.
- Establish MLOps and LLMOps practices covering evaluation, versioning, guardrails, monitoring, rollback, incident response, and release governance.
- Embed responsible AI, privacy, security, auditability, explainability, model-risk, and regulatory-compliance controls.
- Lead discovery workshops, value framing, solution estimation, PoCs, business cases, acceptance criteria, and transitions to managed operations.
- Coach architects, engineers, data scientists, process SMEs, security teams, and operations leads across EY, client, and partner organizations.
- Create reusable architecture playbooks, delivery patterns, demo journeys, and managed-service assets for ASEAN industries and service lines.
Requirements
- 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 of Microsoft Azure, AWS, or Google Cloud Platform, including AI, data, identity, security, networking, and deployment services.
- Practical software engineering capability in Python and one or more of TypeScript, Java, C#, or Go, with strong knowledge of APIs, microservices, integration design, and testing strategies.
- Experience with LLMOps/MLOps practices including evaluation, prompt and model versioning, model registries, monitoring, CI/CD, guardrails, and release governance.
- Knowledge of enterprise workflow and automation patterns across HR, Finance, Procurement, Supply Chain, or shared-services operations.
- Understanding of security, privacy, responsible AI, data residency, access control, audit logging, and model-risk considerations.
- Client-facing consulting experience with structured problem solving, executive communication, workshop facilitation, solution shaping, and delivery leadership.
- Preferred experience with managed-services or shared-services operating models, agent frameworks, orchestration tools, workflow platforms, enterprise integrations, vector databases, search technologies, and regulated industries.
- Relevant cloud or architecture certifications such as Azure Solutions Architect, Azure AI Engineer, AWS Solutions Architect, AWS Machine Learning, Google Professional Cloud Architect, or Google Professional Machine Learning Engineer.
Benefits
- Singapore-based role with ASEAN travel as needed for client delivery, pursuits, workshops, and regional leadership engagements.
- Relocation support may be available for the right candidate.
Tech Stack
Amazon DynamoDBApache AirflowAWSAzureC#ElasticsearchGitHub ActionsGitLab CI/CDGoGoogle BigQueryGoogle Cloud PlatformGrafanaGraphQLJavaKubernetesPrometheusPythonRedisTerraformTypeScript
Categories
Solutions Engineering