Business Performance - Supply Chain Manufacturing Operations Solution - Senior Manager - Consulting

Location: Anywhere in Country

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The opportunity

As a Solution Senior Manager in Supply Chain Manufacturing Operations, you will lead the design, build, testing, and scaling of differentiated AI-enabled manufacturing solutions. You will combine deep manufacturing and supply chain 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 senior, hands‑on solution leadership role. The primary purpose of the position is to set direction and lead teams that 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 lead and collaborate with manufacturing practitioners, Managers, 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. You will be accountable for solution strategy, technical credibility, quality and risk management, commercial relevance, talent development, repeatability, and adoption across the practice.

Your key responsibilities

As a Solution Senior Manager in Supply Chain Manufacturing Operations, you will be responsible for the strategy, portfolio direction, and end‑to‑end development of AI-enabled manufacturing solutions and the reusable data and technology foundations required to scale them across pursuits and engagements.

• Set the vision, roadmap, investment priorities, and quality standards for a portfolio of AI-enabled manufacturing solutions, prototypes, accelerators, demonstrations, and reusable solution components.
• Lead Managers and multidisciplinary teams through concept definition, architecture, build, testing, release, adoption, and continuous improvement while remaining sufficiently hands‑on to challenge technical decisions and resolve critical issues.
• Translate manufacturing and supply chain priorities into compelling AI use cases, value hypotheses, user stories, functional and technical requirements, data and model requirements, acceptance criteria, and measurable operational outcomes.
• Own solution governance, including architecture decisions, responsible AI, cybersecurity, data privacy, quality, risk, release readiness, documentation, and compliance with firm development standards.
• Direct the design of manufacturing data platforms that connect and contextualize data from MES/MOM, ERP, historians, SCADA, PLC, IoT, quality, maintenance, laboratory, warehouse, engineering, document, image, and enterprise systems.
• Guide the development of reusable manufacturing data models, ontologies, semantic layers, taxonomies, metadata, entity relationships, knowledge graphs, and governance standards spanning assets, products, materials, production, quality, maintenance, inventory, energy, labor, and performance.
• Lead the design and industrialization of RAG and GraphRAG solutions using governed manufacturing content, operational data, embeddings, vector search, knowledge graphs, metadata filtering, evaluation methods, guardrails, and human oversight.
• Shape AI assistants, copilots, agents, predictive models, and intelligent workflows for use cases such as predictive maintenance, anomaly detection, root‑cause analysis, quality investigation, production optimization, shift handover, troubleshooting, energy optimization, and operational decision support.
• Lead the configuration and extension of SymphonyAI industrial capabilities and comparable industrial data and AI platforms, including data foundations, unified namespace patterns, knowledge graphs, industrial AI models, copilots, agent workflows, and low‑code or no‑code applications.
• Oversee integrations and reusable connectors using APIs, event streams, industrial protocols, data pipelines, orchestration tools, and common IT/OT integration patterns.
• Establish engineering standards for reusable code, source control, configuration management, model versioning, data quality, testing, DevOps, DataOps, MLOps, LLMOps, security, release management, and solution documentation.
• Own solution backlogs and product roadmaps; prioritize features, define releases, manage technical dependencies, allocate resources, and coordinate contributors through agile development cycles.
• Partner with senior practice, account, pursuit, alliance, and delivery leaders to identify market needs, shape differentiated offerings, estimate effort and investment, support proposals and demonstrations, and enable successful adoption.
• Support technical sales and business development by leading solution discovery and technical qualification, shaping architectures and implementation approaches, developing compelling demonstrations and proofs of concept, contributing to proposals, statements of work, estimates, pricing inputs, and oral presentations, and articulating the differentiated value, feasibility, scalability, and risk profile of proposed manufacturing solutions to client and internal stakeholders.
• Contribute to revenue generation by identifying opportunities, shaping the solution and value proposition, supporting proposal development and pricing, and building trusted relationships with internal and selective client stakeholders.
• Manage solution‑development budgets, staffing, milestones, risks, dependencies, and investment decisions; communicate progress, outcomes, and escalation needs to senior stakeholders.
• Package solutions for reuse through reference implementations, technical documentation, configuration guides, architecture diagrams, data‑model specifications, test assets, deployment guidance, and enablement materials.
• Serve as a senior solution expert for pursuits and delivery teams while remaining primarily accountable for internal solution engineering rather than ongoing engagement delivery.
• Lead, coach, and develop Managers, engineers, analysts, and specialists; provide timely feedback, support career development, strengthen inclusive teaming, and build the next generation of manufacturing solution leaders.

Skills and attributes for success

To excel in this role, you will need a builder mindset, senior leadership presence, and the ability to move from an ambiguous manufacturing problem to a technically credible, commercially relevant, reusable solution while directing teams and influencing stakeholders.

• Deep manufacturing and supply chain credibility with the ability to connect operating model, process, data, technology, workforce, and business‑value considerations.
• Senior‑level solution‑engineering leadership across manufacturing operations, industrial data, AI, applications, integration, cybersecurity, and deployment.
• Strong understanding of manufacturing data platforms, industrial DataOps, unified namespaces, data fabrics, data products, contextualization, semantic modeling, ontologies, knowledge graphs, and edge‑to‑cloud architectures.
• Practical experience directing RAG or GraphRAG solutions, including ingestion, chunking, embeddings, vector and graph retrieval, reranking, grounding, prompting, evaluation, observability, and responsible AI controls.
• Strong data‑modeling and architecture skills across conceptual, logical, physical, semantic, time‑series, event, graph, and application models, including alignment with ISA‑95, ISA‑88, asset hierarchies, and manufacturing process models.
• Ability to evaluate predictive, prescriptive, generative, and agentic AI opportunities for feasibility, value, data readiness, workflow fit, adoption, scalability, and risk.
• Working knowledge of MES/MOM, historians, SCADA, PLC, CMMS/EAM, LIMS, QMS, ERP, warehouse, planning, engineering, connected‑worker, cloud, database, streaming, API, and industrial protocol technologies.
• Strong product‑management and agile‑development capability, including portfolio strategy, roadmap and backlog management, investment prioritization, feature definition, release planning, and iterative prototyping.
• Demonstrated ability to lead Managers and multidisciplinary teams, manage competing priorities, and maintain high standards for technical quality, usability, security, risk, and manufacturing outcomes.
• Executive communication, structured problem‑solving, systems thinking, facilitation, negotiation, and the ability to influence senior business and technical stakeholders.
• Commercial awareness and experience supporting business development, proposals, estimates, staffing models, financial planning, alliance relationships, and solution investment decisions.
• Ability to assess solution quality through architecture reviews, functional testing, model and retrieval evaluation, performance and security review, and user validation.
• Commitment to coaching, inclusive leadership, talent development, knowledge sharing, and building high‑performing teams.

To qualify for the role, you must have

• A bachelor’s degree in engineering, computer science, data science, information systems, manufacturing, supply chain, operations, or a related discipline.
• No less than 5–7 years of relevant experience spanning AI solution development, industrial data platforms, digital manufacturing, manufacturing technology, supply chain consulting, 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.
• Demonstrated experience leading Managers and multidisciplinary technical teams through iterative solution‑development cycles, including work planning, delegation, review, feedback, performance management, and capability building.
• Experience influencing senior stakeholders, supporting pursuits and commercial decisions, managing solution‑development risks and investments, and operating effectively in a primarily internal solution‑building role with selective client interaction.
• Ability to travel up to 50%.

Ideally, you’ll also have

• 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