AI Solutions Lead, Manufacturing Operations

EY; San Jose (CA); Remote; USD 170,000 - 210,000; Full time

Job summary

EY is seeking a Solution Manager in Supply Chain Manufacturing Operations to design, build, test, and scale AI‑enabled manufacturing solutions. You will combine domain knowledge with data platforms, industrial architectures, and generative AI to create prototypes, accelerators, reference architectures, and reusable IP that can be configured by pursuit and delivery teams.

You will collaborate with data engineers, data scientists, AI engineers, software developers, and solution leaders to turn

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 contextualize 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, labor, 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 operationalize 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 optimization, shift handover, troubleshooting, work‑instruction retrieval, energy optimization, 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, contextualization, semantic modeling, 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, prioritize 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

Job description

EY is seeking a Solution Manager in Supply Chain Manufacturing Operations to design, build, test, and scale AI‑enabled manufacturing solutions. You will combine domain knowledge with data platforms, industrial architectures, and generative AI to create prototypes, accelerators, reference architectures, and reusable IP that can be configured by pursuit and delivery teams.

You will collaborate with data engineers, data scientists, AI engineers, software developers, and solution leaders to turn