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Job description
Location: Anywhere in Country
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The opportunity
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.
Your Key Responsibilities
As a Solution Manager in Supply Chain Manufacturing Operations, you will be responsible for the end-to-end development of AI-enabled manufacturing solutions and the reusable data and technology foundations required to scale them.
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 And Attributes For Success
To excel in this role, you will need a builder mindset and the ability to move from an ambiguous manufacturing problem to a working, technically credible, reusable solution.
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, contextualisation, 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.
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Worksite address
columbus, OH, 43224, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.