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

EY•Hartford, DC

27d•$128,400 - $267,400

About The Position

As a Solution Manager in Supply Chain Manufacturing Operations, you will design, build, test, and scale differentiated AI-enabled manufacturing solutions. This hands-on solution-building role combines 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. The primary purpose of the position is to create working solutions, prototypes, accelerators, demonstrations, reference architectures, and reusable intellectual property that pursuit and delivery teams can configure and deploy. You will collaborate 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 is measured by the quality, technical credibility, usability, repeatability, and adoption of the solutions built. The role is primarily focused on internal solution engineering rather than ongoing client-facing delivery. It includes selective interaction with internal stakeholders, alliance partners, pursuit teams, and delivery teams to gather requirements and transfer knowledge. Travel of up to 50% may be required.

Requirements

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.
Ability to travel up to 50%.
Hands-on solution-engineering capability across manufacturing operations, industrial data, AI, applications, integration, and deployment.
Strong understanding of manufacturing data platforms, industrial DataOps, unified namespaces, data fabrics, data products, contextualization, and edge-to-cloud architectures.
Practical knowledge of knowledge graphs, graph databases, semantic layers, ontologies, taxonomies, metadata, entity resolution, lineage, and data governance.
Experience designing RAG or GraphRAG solutions, including document ingestion, chunking, embeddings, vector databases, graph retrieval, reranking, grounding, prompting, evaluation, observability, and responsible AI controls.
Strong data-modeling skills across conceptual, logical, physical, semantic, time-series, event, graph, and application data models.
Ability to design manufacturing ontologies and common data models aligned with standards and concepts such as ISA-95, ISA-88, asset hierarchies, material models, process models, quality models, and maintenance models.
Experience building predictive, prescriptive, generative, and agentic AI solutions for industrial or manufacturing applications.
Ability to develop, configure, integrate, and test working applications, copilots, agents, workflows, dashboards, APIs, and reusable solution services.
Working knowledge of MES/MOM, historians, SCADA, PLC, CMMS/EAM, LIMS, QMS, ERP, warehouse, planning, engineering, and connected-worker systems.
Understanding of data pipelines, stream processing, APIs, industrial protocols, data lakes or lakehouses, relational databases, time-series databases, document stores, vector databases, and graph databases.
Familiarity with software engineering, DevOps, DataOps, MLOps, and LLMOps practices for moving AI solutions from prototype toward scalable deployment.
Ability to assess solution quality through functional testing, model evaluation, retrieval evaluation, performance testing, security review, and user validation.
Strong product-management and agile-development skills, including backlog management, feature definition, release planning, acceptance criteria, and iterative prototyping.
Ability to communicate technical designs clearly through concise architecture diagrams, specifications, demonstrations, and reusable documentation.
Effective collaboration with engineers, data scientists, architects, manufacturing subject-matter specialists, alliance partners, and internal solution owners.
Ability to manage multiple solution-development priorities while maintaining technical quality, usability, security, and alignment with manufacturing outcomes.

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 AI use cases, user stories, functional and technical requirements, data and 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 connecting and contextualizing data from MES/MOM, ERP, historians, SCADA, PLC, IoT, quality, maintenance, laboratory, warehouse, engineering, document, image, and enterprise systems.
Develop reusable manufacturing data models covering 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.
Build and operationalize manufacturing knowledge graphs connecting structured, time-series, event, document, engineering, and unstructured data 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, including 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 supporting 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 showing how manufacturing data foundations, AI models, copilots, agents, and applications work together.
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.