4 Days onsite at Dearborn, MI - only W2, No C2C

Skills Required:
Artificial Intelligence & Expert Systems, Machine Learning, Data Science, Data Modeling, Software Development Lifecycle
Experience Required:
Specialist Exp: 5+ experience in relevant field
Experience Preferred:
Minimum Requirements • Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field. • 3+ years of progressive experience in AI/ML, data science, or advanced analytics, with a proven track record of delivering production-grade solutions in large enterprise environments. • Strong proficiency in Python and SQL. Familiarity with Graph Query Languages (e.g., Cypher). • Demonstrated experience with MLOps principles and tools (e.g., Azure ML, AWS SageMaker, GCP AI Platform, Kubeflow, MLflow) and designing / implementing AI-specific SDLCs. • Strong technical expertise in cloud services (GCP/Vertex AI) and data integration patterns • Strong analytical, problem-solving, and critical thinking skills. • Exceptional communication, interpersonal skills, and stakeholder management skills. Preferred Qualifications • AI-SDLC Experience: Proven track record of using AI tools to enhance personal or team productivity (e.g., Agentic workflows, RAG-based requirement synthesis). • Requirement Engineering: Experience in a product engineering role with proven track record of translating business needs into technical specifications for applied AI implementation. • Knowledge Graph: Understanding semantic ontologies and how they enable advanced analytics. • COTS Integration: Experience integrating COTS AI solutions into an enterprise tech stack. • Supply Chain Domain Knowledge: Functional understanding of supply chain operations, including demand & capacity planning, logistics, sustainability & risk management, resilience, etc.
Education Required:
Bachelor's Degree
Education Preferred:
Master's Degree
Additional Information:
Hybrid Position 4 days a week onsite • Business Requirement Gathering: Partner with supply chain functional leads to elicit and document business requirements and translate them into technical specifications for AI-driven decision support tools, ensuring every solution delivers measurable business value. • Model Integration & Deployment: Act as the primary technical lead for applied AI implementation. Take pre-developed models from internal partners or 3rd-party vendors (COTS) and successfully deploy them within the supply chain GCP space. • Graph-Based AI Implementation: Work closely with Knowledge Graph engineering teams to map model inputs/outputs to enterprise ontologies. Execute model inference against graph data to provide prescriptions for N-tier supplier risk and material movement. • AI-Driven SDLC Execution: Champion and implement AI-assisted development practices. Use LLM-based tools (e.g., GitHub Copilot, automated PR agents, and AI-generated documentation) to accelerate delivery and ensure high code quality. • Pipeline & MLOps Engineering: Design the "connective tissue " between Knowledge Graph updates and model inference engines. Maintain automated pipelines that ensure decision-support tools are always powered by the most current data. • Technical Standardization: Develop reusable integration patterns and data contracts to ensure that AI solutions can be scaled across multiple business units without redundant engineering effort.