AI/ML and Data Engineer
Indexed description
A critical component of this role is end-to-end delivery: translating manufacturing-focused opportunities into secure, production-grade AI solutions (including traditional ML and LLM-enabled applications), building the underlying data foundation to support analytics and decision-making, and providing client-facing consulting services that demonstrate measurable value and responsible use of AI.
The position also contributes to organizational capability-building by coaching stakeholders on appropriate AI use, shaping standards-aligned governance practices, and representing SME’s point of view on AI and data in manufacturing through partner engagement and industry thought leadership.
MAJOR FUNCTIONS:
- Lead the design, development, and deployment of manufacturing-focused AI solutions, including predictive maintenance, anomaly detection, process optimization, and related applied machine learning use cases.
- Architect and deliver LLM-enabled Generative AI solutions (e.g., Retrieval-Augmented Generation, tool use, and agentic workflows) that enable natural-language access to SME knowledge assets such as research content, standards-related material, membership and event data, and learning resources.
- Build and maintain a modern, scalable data platform that ingests, curates, and governs structured and unstructured manufacturing-related datasets, ensuring data quality, metadata management, lineage, and appropriate access controls.
- Design and implement database patterns (relational, lakehouse, and vector databases) to support analytics, AI development, and reliable retrieval across SME content and partner datasets.
- Establish and mature MLOps/LLMOps practices, including CI/CD, model and prompt versioning, monitoring/observability, rollback procedures, and cost/performance optimization for production environments.
- Define evaluation approaches and quality controls for AI systems, including performance metrics, monitoring for drift, and iterative improvement loops that maintain reliability and trust over time.
- Oversee analytics engineering (ETL/ELT) supporting dashboards and reporting that inform SME decision-making on industry trends, program outcomes, member engagement, and organizational performance.
- Conduct discovery workshops with manufacturers, members, and partners to identify priority AI/data opportunities, frame ROI and feasibility, and develop actionable implementation roadmaps.
- Lead pilot and proof-of-value engagements from requirements through deployment and knowledge transfer, ensuring solutions are supportable, secure, and aligned with stakeholder needs.
- Provide executive-level advisory services on AI adoption and data modernization, tailoring recommendations for both technical and non-technical stakeholders and enabling informed decision-making.
- Author proposals, statements of work, technical approaches, and executive readouts that communicate scope, risks, outcomes, and measurable impact of AI/data initiatives.
- Implement and champion AI/data governance and security practices aligned with relevant frameworks, including privacy safeguards, auditability, and responsible AI principles (bias mitigation, explainability, and safe use guidance).
- Partner with internal stakeholders (e.g., workforce development, membership, research, standards-related groups, events, and marketing) to ensure AI/data initiatives align with SME priorities and deliver clear value to the manufacturing community.
- Mentor team members and/or contractors in AI/data delivery best practices to improve consistency and execution quality.
- Serve as an internal subject matter expert, enabling appropriate use of AI tools through guidance, enablement materials, and practical coaching.
- Co-manage relationships with external technology partners, cloud providers, and vendors to ensure effective delivery, scalability, and cost discipline.
- Represent SME at industry events and partner forums, providing thought leadership on AI and data in manufacturing and supporting SME’s role as a trusted, industry-facing nonprofit.
- Other duties as assigned.
- Bachelor’s degree required in Computer Science, Engineering, Data Science, or related field; advanced degree preferred.
- At least 8 years of progressive experience in AI/ML engineering, including a minimum of 3 years deploying traditional ML and Generative AI/LLM solutions into production.
- Demonstrated track record building, shipping, and operating production AI systems using modern frameworks and best practices.
- Extensive data engineering experience, including pipeline development, database design, and management of large-scale datasets.
- Strong expertise with cloud platforms (AWS, Azure, or GCP) and associated data and AI services.
- Demonstrated consulting and client-facing delivery experience, including workshop facilitation, requirements elicitation, and technical advisory execution.
- Proven ability to lead cross-functional initiatives as a senior individual contributor and/or people leader, with strong collaboration skills across technical and business teams.
- Exposure to manufacturing processes, systems, and operational challenges through industry experience or consulting engagements.
- Working knowledge of data security, privacy, and governance practices; ability to implement controls appropriate for sensitive data and partner environments.
- Excellent communication and presentation skills, with the ability to translate complex technical topics for varied audiences and senior stakeholders.
- Experience with digital thread/digital twin concepts, manufacturing simulation, or related manufacturing systems integration.
- Knowledge of manufacturing standards, technical publications, quality/reliability engineering, or certification/adherence environments.
- Proficiency with platforms such as Snowflake, Databricks, Azure OpenAI, and/or AWS Bedrock.
- Experience with compliance frameworks (e.g., SOC 2, NIST) and/or privacy regulations as applied to data and AI systems.
- Advanced AI experience, including fine-tuning open-source LLMs for domain-specific manufacturing applications.
- Familiarity with operational technology (OT) concepts and the realities of manufacturing data environments.
- Experience developing AI solutions that incorporate manufacturing domain knowledge into model design, evaluation, and deployment approaches
- Communication and Collaboration
- Relationship Management
- Professionalism and Integrity
- Critical Thinking and Decision-Making
- Execution
- Initiative, Leadership, and Development
- AI Architecture and Solution Design
- Data Architecture and Governance Implementation
- AI Lifecycle and Model Operations Management
- Responsible AI and Risk Mitigation
- Technical Consulting and Stakeholder Advisory
- External Partner and Vendor Collaboration
- Normal office environment
- Regular, in-person attendance required
- Primary office location: Southfield, MI
- Travel required (up to 25%)
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