Business Performance - Supply Chain Manufacturing Ops & AI Transformation Delivery - Manager

EY•Hartford, DC

6d•$128,400 - $267,400•Remote

About The Position

As a Manager in our Supply Chain Manufacturing practice, you will lead market-facing delivery of manufacturing transformation programs that combine operational improvement, digital technology, advanced analytics, and AI. You will work directly with client executives, plant leaders, operators, engineers, quality, maintenance, supply chain, IT, OT, data, and AI teams to turn business priorities into implementable solutions that improve productivity, reliability, quality, visibility, decision-making, and manufacturing agility. You will help clients establish the trusted data and knowledge foundations required to scale predictive, generative, and agentic AI across plant and network operations.

Requirements

A bachelor's degree in engineering, supply chain, operations, information systems, computer science, business, or a related field.
At least 4 to 6 years of relevant experience in digital manufacturing, manufacturing operations, supply chain, industrial technology, systems implementation, consulting, or a related environment.
Experience shaping or delivering AI, advanced analytics, or knowledge-enabled solutions within manufacturing, supply chain, engineering, quality, maintenance, or industrial operations.
Experience working with manufacturing data models, semantic models, ontologies, knowledge graphs, RAG solutions, or comparable approaches for contextualizing and governing operational data.
Experience translating AI use cases into data, knowledge, architecture, integration, security, evaluation, adoption, and value-realization requirements.
Experience leading client-facing workstreams or projects that connect manufacturing processes with technology implementation.
Experience translating between functional stakeholders and technical teams, with ownership of delivery artifacts such as process maps, requirements, functional designs, data mappings, interface requirements, test strategies, deployment plans, or value cases.
Experience managing multidisciplinary teams, delivery plans, project risks, quality, and stakeholder communications.
Ability and willingness to travel to client and manufacturing sites as required by engagement needs.
Strong written and verbal communication skills, with the ability to influence business, engineering, IT, OT, vendor, and leadership stakeholders.

Nice To Haves

Experience in life sciences, consumer products, chemicals, automotive, aerospace and defense, industrial products, or advanced manufacturing.
Experience with manufacturing operating models, network optimization, Lean, Six Sigma, TPM, IWS, reliability, cost optimization, contract manufacturing, or vertical start-up.
Experience with manufacturing standards and reference models such as ISA-88, ISA-95, OPC UA information models, asset administration shells, or comparable industrial ontologies and data-modeling standards.
Working knowledge of IT/OT convergence, industrial cybersecurity considerations, cloud or edge patterns, and shop-floor connectivity.
Hands-on exposure to graph databases, vector databases, semantic technologies, metadata platforms, industrial data fabrics, unified namespace architectures, digital twins, or AI development platforms.
Experience designing or implementing RAG, GraphRAG, manufacturing copilots, intelligent agents, natural-language interfaces, or AI-enabled decision-support workflows.
Experience establishing AI evaluation frameworks, governance controls, reusable prompt and retrieval patterns, knowledge-curation processes, or MLOps/LLMOps capabilities.
Familiarity with computer vision, time-series machine learning, anomaly detection, optimization, simulation, predictive maintenance, quality analytics, or edge AI.
Experience in regulated manufacturing, computer system validation, data integrity, or quality and compliance requirements.
Experience developing reusable solution assets, methods, reference architectures, demonstrations, accelerators, or enablement materials.
Relevant platform, project management, Agile, Lean, Six Sigma, or manufacturing certifications.

Responsibilities

Lead client-facing manufacturing transformation workstreams and programs from opportunity shaping through design, implementation, deployment, and value realization.
Act as the two-way liaison between design and delivery teams by translating client needs into solution requirements and translating reusable capabilities into practical engagement plans.
Partner with the design the team prioritize solution enhancements, validate use cases, shape demonstrations, assess implementation readiness, and define the documentation, training, and support needed for scalable delivery.
Capture lessons, recurring requirements, configuration patterns, technical constraints, and delivery feedback from engagements and incorporate them into the design backlog and solution roadmap.
Assess manufacturing processes, performance gaps, user needs, data flows, controls, and technology constraints across plant and network environments.
Translate manufacturing priorities into process designs, functional requirements, user stories, data requirements, integration requirements, acceptance criteria, deployment roadmaps, and measurable outcomes.
Guide solution design across ERP, MES/MOM, connected worker, SCADA, historians, LIMS, QMS, EAM/CMMS, WMS, APS, industrial data platforms, knowledge platforms, AI services, analytics, and reporting environments, based on engagement needs.
Lead functional design, configuration oversight, prototyping, testing, validation, cutover, training, change adoption, hypercare, and benefits tracking.
Facilitate workshops and decision forums across operations, engineering, quality, maintenance, supply chain, IT, OT, cybersecurity, data, and technology-vendor stakeholders.
Manage project scope, plans, resources, economics, risks, dependencies, decisions, quality, and executive communications.
Lead and coach multidisciplinary delivery teams, review work products, and establish clear accountability for outcomes.
Support technical sales through solution shaping, demonstrations, estimates, proposals, implementation approaches, and responses to requests for proposal.
Identify follow-on opportunities based on client outcomes and emerging manufacturing priorities while maintaining trusted client relationships.
Shape and deliver manufacturing AI use cases such as predictive maintenance, quality intelligence, root-cause analysis, process optimization, intelligent scheduling, energy optimization, knowledge assistants, copilots, and AI-enabled frontline workflows.
Design manufacturing data and knowledge foundations that connect structured, unstructured, time-series, event, image, document, and engineering data across plant, edge, and cloud environments.
Lead the definition of manufacturing ontologies, common data models, semantic models, and semantic layers covering assets, equipment hierarchies, materials, products, orders, batches, recipes, processes, quality events, maintenance records, people, locations, and performance measures.
Guide the implementation of knowledge graphs that connect operational entities, relationships, events, documents, and business rules to support contextual search, multi-hop reasoning, traceability, explainability, and reusable AI services.
Define and implement retrieval-augmented generation approaches, including document RAG, hybrid retrieval, and graph-augmented RAG, using vector search, metadata, semantic relationships, and governed source content to ground AI responses.
Translate manufacturing knowledge into machine-readable structures, retrieval strategies, prompts, agent instructions, decision rules, and reusable context services that improve AI relevance and reduce unsupported outputs.
Establish AI delivery and governance requirements covering data quality, lineage, provenance, access controls, model evaluation, human oversight, cybersecurity, intellectual property, regulatory compliance, monitoring, and responsible AI.
Plan and execute AI pilots from use-case prioritization and value framing through data readiness, prototyping, evaluation, deployment, adoption, benefits tracking, and scaling across sites.