Description

Key Responsibilities

• Translate supply chain problems (forecasting, supplier risk, planner copilots, exception triage, document extraction, scheduling, optimization, and similar) into well-scoped AI projects with explicit success criteria
• Own end-to-end technical execution: solution design, data assembly, model or agent build, integration, validation, and production hand-off
• Stand up and maintain data foundations across ERP, MES, PLM, supplier portals, and external signals; profile, clean, and document quality and lineage
• Select the right approach for each problem — rules, heuristics, optimization, time-series, classical ML, LLMs, RAG, or agents — and combine them where it produces the best outcome
• Establish deterministic baselines and quantify AI lift with appropriate statistical rigor; validate continuously, not only at project start
• Build, deploy, and iterate on solutions with real users; instrument adoption, outcomes, and failure modes
• Harden code, data pipelines, and integrations for broader rollout across users, sites, or business units
• Partner with platform, data, and MLOps teams on deployment patterns, monitoring, alerting, and operational hand-off
• Run multiple concurrent projects by leveraging reusable scaffolding, evaluation harnesses, and templates
• Communicate clearly to technical and business audiences — including executive-ready status, evidence, and recommendations

Deliverables

• Solution design notes covering problem, approach, data, assumptions, risks, and integration points
• Data foundation documentation – sources, lineage, quality, and gaps
• Working AI capabilities deployed to real users, with quantified business impact
• Validation and evaluation reports – metrics, baselines, failure modes, sensitivity analysis
• Production-ready code, pipelines, and integration artifacts
• Operational hand-off packages for platform / MLOps teams (monitoring playbooks, runbooks, dependencies)
• Reusable patterns, components, and frameworks that benefit future projects

Required Qualifications

• 7+ years in AI/ML or data engineering, with a track record of shipping AI capabilities into production — not only prototypes
• Strong Python and SQL; comfort across the data $B"*(B model $B"*(B integration $B"*(B output stack
• Hands-on experience with at least two of: LLMs/agents, forecasting, optimization, classical ML, document/NLP extraction
• Demonstrated ability to take AI systems from concept through pilot to production, including hardening, integration, and operational hand-off
• Skill in working with ambiguous problems, imperfect and evolving enterprise data, and shifting requirements
• Excellent written and verbal communication; able to defend results to both skeptical technical reviewers and senior business stakeholders

Preferred Qualifications

• Prior supply chain, manufacturing, or operations exposure in the semiconductor or high-tech industries
• Experience with ERP, MES, and PLM data structures (SAP, Oracle, Kinaxis, or similar)
• Familiarity with LLM evaluation, RAG patterns, agent frameworks, and vector stores
• Experience building hybrid systems that combine rules, heuristics, and AI
• History of scaling AI across multiple sites, users, or business units
• Experience shaping reusable AI delivery frameworks, components, or internal platforms

Key Skills

Education

Bachelor's degree