Manufacturing AI Solutions Lead

EY; Portland (OR); On-site; USD 140,000 - 210,000; Full time

Job summary

EY is seeking a Solution Manager in Supply Chain Manufacturing Operations to design, build, test, and scale differentiated AI-enabled manufacturing solutions. You will combine manufacturing knowledge with data platforms, ontologies, and knowledge graphs to create reusable solution components.

This hands-on role involves end-to-end development, from problem definition to industrialization, working with engineers, data scientists, and solution leaders to deliver production-ready assets.

Qualifications

• Design AI-based manufacturing solutions and prototypes.
• Translate manufacturing problems into AI use cases and requirements.
• Lead end-to-end development from concept to prototype and handoff.
• Develop data models and ontologies for manufacturing.
• Build knowledge graphs and RAG-enabled solutions.
• Configure and extend industrial data foundations and integrations.

Responsibilities

• Own the design, hands-on development, testing, documentation, and continuous improvement of AI-based manufacturing solutions, prototypes, accelerators, demonstrations, and reusable solution components.
• Translate manufacturing problems into clearly defined AI use cases, user stories, functional requirements, technical requirements, data requirements, model requirements, acceptance criteria, and measurable operational outcomes.
• Lead use-case development from concept through working prototype, including problem definition, value hypothesis, process design, data assessment, solution architecture, configuration, model integration, testing, validation, and handoff for industrialization.
• Design manufacturing data platform architectures that connect and contextualise data from MES/MOM, ERP, historians, SCADA, PLC, IoT, quality, maintenance, laboratory, warehouse, engineering, document, image, and enterprise systems.
• Develop reusable manufacturing data models spanning assets, sites, lines, equipment, products, materials, production orders, process parameters, quality events, maintenance activities, inventory, energy, labour, and performance measures.
• Define and implement manufacturing data ontologies, semantic models, taxonomies, metadata, entity relationships, and governance standards that establish consistent meaning across plants, systems, and use cases.
• Build and operationalise manufacturing knowledge graphs that connect structured, time-series, event, document, engineering, and unstructured data to provide context for analytics, AI models, copilots, and agents.
• Design RAG solutions using governed manufacturing content, operational data, knowledge graphs, embeddings, vector search, metadata filtering, prompt patterns, and evaluation methods.
• Build AI assistants, copilots, agents, predictive models, and intelligent workflows for manufacturing use cases such as predictive maintenance, anomaly detection, root-cause analysis, quality investigation, production optimisation, shift handover, troubleshooting, work-instruction retrieval, energy optimisation, and operational decision support.
• Develop prompt libraries, retrieval strategies, grounding approaches, evaluation datasets, guardrails, human-in-the-loop controls, traceability mechanisms, and monitoring standards for generative AI solutions.
• Configure and extend SymphonyAI industrial capabilities, including manufacturing data foundations, unified namespace patterns, knowledge graphs, industrial AI models, RAG-enabled copilots, agent workflows, and low-code or no-code applications.
• Apply comparable industrial data and AI platforms when appropriate, including platforms that support industrial DataOps, contextualisation, semantic modelling, knowledge graphs, MLOps, generative AI, agent orchestration, edge-to-cloud integration, and application development.
• Build integrations and reusable connectors using APIs, event streams, industrial protocols, data pipelines, orchestration tools, and common IT/OT integration patterns.
• Establish development standards for source control, reusable code, configuration management, model versioning, data quality, testing, release management, DevOps, MLOps, LLMOps, security, and solution documentation.
• Create reference implementations and demonstration environments that show how manufacturing data foundations, AI models, copilots, agents, and applications work together in an integrated solution.
• Evaluate emerging AI, data, and industrial technology capabilities through practical experiments and convert relevant capabilities into working solution components and product roadmaps.
• Maintain solution backlogs, prioritise features, define releases, manage technical dependencies, and coordinate multidisciplinary contributors through agile solution-development cycles.
• Package solutions for reuse through technical documentation, configuration guides, architecture diagrams, data-model specifications, test scripts, deployment guidance, and enablement materials.
• Provide technical support to pursuit and delivery teams as a solution expert while remaining primarily accountable for internal solution engineering rather than ongoing client-facing delivery.
• Coach developers, engineers, analysts, and specialists contributing to AI manufacturing solution development.

Skills

AI-enabled Manufacturing

Data platforms

Knowledge graphs

RAG

GraphRAG

MLOps

LLMOps

Solution design

Cross-functional teamwork

Job description

EY is seeking a Solution Manager in Supply Chain Manufacturing Operations to design, build, test, and scale differentiated AI-enabled manufacturing solutions. You will combine manufacturing knowledge with data platforms, ontologies, and knowledge graphs to create reusable solution components.

This hands-on role involves end-to-end development, from problem definition to industrialization, working with engineers, data scientists, and solution leaders to deliver production-ready assets.