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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer - **Company:** Ford Motor Company - **Location:** Dearborn, MI, United States - **Experience:** Expert - **Salary:** $99,600.0 - $192,900.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Cloud Computing, Code Review, Computer Engineering, Continuous Integration, Information Engineering, Information Leak Prevention, Memory Management, Fuzz Testing, Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Management of Software Versions, Data Logging, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Agentic-AI, Build Management, Containerization, Git Flow, Information Technology, AutoGen, Machine Learning Operations, Semantic Kernel, Human in the Loop - **Published:** October 4, 2026 - **Apply:** https://www.careerjet.com/job/usb914cd54b77e76d7f99c89e25e14f598/eaa ## About the Role * Education: Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a related technical field, or equivalent experience. * Technical Experience: 5+ years in software engineering, ML engineering, or applied AI, including hands-on Python, SQL, and experience building or deploying LLM/agent-based applications (e.g., prompt engineering, fine-tuning, RAG, or frameworks such as LangChain, LangGraph, AutoGen, Semantic Kernel). * Software Engineering Practices: Experience developing maintainable production services/APIs, with working knowledge of automated testing, Git workflows, code reviews, CI/CD, and containerization. * Evaluation Discipline: Experience defining measurable quality criteria and evaluating AI/ML systems using quantitative metrics and structured human review. * Collaborative Delivery: Proven ability to work with software engineering teams and business users to move solutions from concept to production. * User-Centered Practice: Demonstrated experience engaging directly with end users to shape and validate AI solutions. * Communication: Proven ability to translate ambiguous business problems into technical solutions and interpret results for non-technical partners. Even better, you may have... * Advanced Degree: Master's degree or PhD in a quantitative or technical field. * Technical Domain Expertise: LLM application architectures (RAG, tool calling, multi-agent orchestration, memory management) and retrieval pipeline design (embeddings, chunking, hybrid search, reranking). * Agent Control & Orchestration: Structured outputs, workflow orchestration, agent state management, and human-in-the-loop controls. * Production Experience: Deploying and maintaining LLM/agent systems in production, including prompt versioning and CI/CD integration. * AI Observability: Implementing logging, tracing, dashboards, and cost/latency monitoring for model or agent behavior. * Security & Adversarial Testing: Testing for prompt injection, data leakage, unsafe tool execution, or unauthorized access. * Cloud & Model Infrastructure: Deploying LLM/agent solutions in a cloud environment (GCP preferred), with familiarity with vector databases and model hosting. * Industry Context: Familiarity with Automotive OEM data, dealer relationships, or supply chain logistics. ## Description The Order Fulfillment Technology team is seeking a Senior AI Engineer to design, build, deploy, and operate production-grade AI solutions supporting forecasting, planning, and scheduling. You'll build retrieval-augmented generation applications and controlled AI agents that generate grounded recommendations and automation with human oversight. This is a hands-on role for someone who can move AI solutions beyond prototypes into secure, scalable, and trustworthy production services. What you'll do... * AI Application Development: Design and build production-grade LLM applications, RAG solutions, and tool-using AI agents to improve decision speed, accuracy, and transparency. * Retrieval Pipelines: Build retrieval pipelines covering ingestion, chunking, embeddings, hybrid search, reranking, grounding, and citations, producing recommendations that are traceable and earn user trust. * Engineering & Deployment: Develop maintainable Python services, APIs, and reusable AI components; deploy and operate them using automated testing, CI/CD, containerization, logging, and monitoring. * Evaluation & Monitoring: Create evaluation datasets and automated tests measuring task success, groundedness, safety, latency, and cost; monitor production for drift, failures, and cost anomalies. * Responsible AI & Governance: Apply responsible controls throughout the development lifecycle, exercising sound judgment on when generative AI is warranted versus a simpler, deterministic solution. * Stakeholder Partnership: Partner with Product, Software Engineering, Data Science, and Data Engineering to bring AI solutions from concept to production. * User Engagement: Spend time directly with planners, schedulers, and other end users to understand real workflows and pain points, ensuring AI solutions are grounded in how decisions are actually made. * Continuous Improvement: Evaluate emerging AI technologies based on measurable business value, quality, and cost. 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