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

Position: Business Performance - Supply Chain - Manufacturing Operations Solutions - Manager - Consulting
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 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 Data Ops, contextualization, semantic modeling, knowledge graphs, MLOps, generative AI, agent orchestration, edge-to-cloud integration, and application development.

+ Build integrations and reusable…