Applied Ai/ml Software Engineer-supply Chain AI and Decision Intelligence

Ford Ford · Auto · Dearborn, MI +1 · Enterprise Technology

Applied AI/ML Engineer to lead Ford's AI-First supply chain transformation by integrating AI models into Enterprise Knowledge Graphs. The role focuses on applied implementation, agentic workflows, and an AI-Driven SDLC to solve complex supply chain problems, manage risk, and build resilience. Responsibilities include business requirement gathering, model integration, graph-based AI implementation, AI-Driven SDLC execution, pipeline/MLOps engineering, and technical standardization.

What you'd actually do

  1. 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.
  2. 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.
  3. Graph-Based AI Implementation: Work closely with Knowledge Graph engineering teams to execute model interface against enterprise ontologies, you will design decision-intelligence frameworks that proactively identify and mitigate risks across the global N-tier supplier network. Simulate "what-if" scenarios using Generative AI and Graph analytics, and enable the supply chain to remain resilient against geopolitical, environmental, and logistical shocks, providing automated prescriptive solutions for supply-chain, logistics and capacity re-allocation before disruptions impact production .
  4. AI-Driven SDLC Execution: Champion and implement AI-assisted development practices. Implement agentic workflows (e.g., AutoGen, CrewAI) and use LLM-based tools (e.g., GitHub Copilot, automated PR agents, and AI-generated documentation) to accelerate delivery with high code quality for the Decision Intelligence platform
  5. Pipeline & MLOps/LLMOps Engineering: Design the "connective tissue" between Knowledge Graph updates and model inference engines. Establish rigorous guardrail frameworks for toxicity, hallucination rates, and latency. Maintain automated pipelines that ensure decision-support tools are always powered by the most current data.

Skills

Required

  • Python
  • SQL
  • MLOps principles and tools
  • designing / implementing AI-specific SDLCs
  • designing, deploying, and maintaining LLM-powered applications in production
  • prompt engineering
  • RAG pipelines
  • safety controls
  • hallucination mitigation
  • observability
  • cost optimization
  • cloud services (GCP/Vertex AI)
  • data integration patterns

Nice to have

  • Graph Query Languages (e.g., Cypher)
  • Agentic workflows
  • Knowledge Graph
  • semantic ontologies
  • COTS Integration
  • Supply Chain Domain Knowledge

What the JD emphasized

  • delivering production-grade solutions
  • AI/ML, data science, or advanced analytics
  • delivering production-grade solutions
  • AI-Driven Software Development Life Cycle (SDLC)
  • LLM-powered applications in production
  • AI-SDLC Experience

Other signals

  • AI-First supply chain transformation
  • applied implementation of AI
  • AI-Driven Software Development Life Cycle (SDLC)
  • agentic workflows
  • LLM-powered applications in production