Business Performance - Supply Chain - Manufacturing Operations Solutions - Manager - Consulting

EY•Hartford, DC

6d•$128,400 - $267,400•Remote

About The Position

As a Solution Manager in Supply Chain Manufacturing Operations, you will design, build, test, and scale differentiated AI-enabled manufacturing solutions. You will combine manufacturing domain knowledge with manufacturing data platforms, industrial data architectures, predictive and generative AI, knowledge graphs, retrieval-augmented generation (RAG), data ontologies, and reusable application components. This is a hands-on solution-building role. The primary purpose of the position is to create working solutions, prototypes, accelerators, demonstrations, reference architectures, and reusable intellectual property that can be configured and deployed by pursuit and delivery teams. You will work with manufacturing practitioners, data engineers, data scientists, AI engineers, software developers, architects, alliance teams, and solution leaders to turn priority manufacturing use cases into production-oriented solution assets. Success will be measured by the quality, technical credibility, usability, repeatability, and adoption of the solutions built.

Requirements

A bachelor’s degree in engineering, computer science, data science, information systems, manufacturing, supply chain, operations, or a related discipline.
At least 4–6 years of relevant experience in AI solution development, industrial data platforms, digital manufacturing, manufacturing technology, data engineering, software development, or a related field.
Demonstrated experience building working AI, data, analytics, or software solutions rather than solely defining strategies, managing programs, or delivering advisory services.
Experience developing manufacturing data platforms, industrial data pipelines, common data models, semantic layers, ontologies, or knowledge graphs.
Experience designing or implementing RAG-based applications, AI copilots, intelligent search, conversational interfaces, or agentic workflows.
Experience with data modeling across operational, manufacturing, engineering, maintenance, quality, or supply chain domains.
Experience taking manufacturing use cases from problem definition through architecture, build, configuration, testing, demonstration, and reusable solution packaging.
Experience integrating data from manufacturing and enterprise systems, including structured, unstructured, time-series, event, image, and document sources.
Experience using programming, scripting, low-code, no-code, data-engineering, AI-development, or application-development tools to produce working solutions.
Experience creating technical documentation, reference architectures, data-model specifications, test plans, demonstrations, and deployment guidance.
Experience leading small technical teams or coordinating multidisciplinary contributors through iterative solution-development cycles.
Ability to work effectively in a primarily internal solution-building role with limited, selective client interaction.

Nice To Haves

Hands-on experience with SymphonyAI Industrial, IRIS Foundry, IRIS Forge, IRIS Flows, industrial copilots, industrial knowledge graphs, or related SymphonyAI capabilities.
Experience with comparable industrial data and AI platforms such as Cognite Data Fusion, Palantir Foundry, Databricks, Microsoft Fabric and Azure AI, AWS industrial and AI services, Google Cloud data and AI services, Snowflake, AVEVA, AspenTech, Siemens, PTC, or similar platforms.
Experience configuring unified namespaces, asset hierarchies, industrial knowledge graphs, governed data catalogs, low-code applications, AI agents, and persona-based copilots.
Experience with graph technologies such as Neo4j, RDF, OWL, SPARQL, property graphs, ontology-management tools, or graph-based retrieval.
Experience with vector databases, embedding models, LLM frameworks, agent frameworks, model gateways, prompt-management tools, and AI evaluation platforms.
Experience building manufacturing AI use cases in predictive maintenance, asset performance, process optimization, quality, vision inspection, production intelligence, connected worker, energy, scheduling, or supply chain.
Knowledge of industrial and manufacturing standards such as ISA-95, ISA-88, OPC UA, MQTT, Sparkplug, IEC 62264, CFIHOS, or related reference models.
Experience with Python, SQL, APIs, JSON, graph query languages, data pipelines, stream processing, cloud services, containers, or application-development frameworks.
Experience applying responsible AI, cybersecurity, access control, data privacy, model governance, content traceability, and human-approval patterns in industrial environments.
Experience with product management, agile development, design thinking, user-centered design, or solution incubation.
Relevant cloud, AI, data, graph, manufacturing, or platform certifications.

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 AI manufacturing solution development.