AI/ML and Data Engineer

Description Responsible for providing both strategic and hands‑on technical leadership for SME's AI/ML, Generative AI, and modern data platforms, advancing SME's nonprofit mission to accelerate adoption of manufacturing technology and strengthen the manufacturing talent pipeline. The incumbent designs, builds, and deploys practical AI capabilities and governed data systems that enable SME insights, products, and services—including workforce development initiatives—while serving as an advisor under the direction of the Director to internal leaders, members, partners, and manufacturers. 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. Requirements MINIMUM EDUCATION, SKILLS, AND EXPERIENCE REQUIREMENTS 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.

PREFERRED

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.

KEY COMPETENCIES

Core Competencies Communication and Collaboration Relationship Management Professionalism and Integrity Critical Thinking and Decision‑Making Execution Initiative, Leadership, and Development Job Competencies 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

WORKING CONDITIONS

Normal office environment Regular, in‑person attendance required Primary office location: Southfield, MI Travel required (up to 25%) #J-18808-Ljbffr SME