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# AI Engineer- Agentic AI Solutions

- **Company:** Ford Motor Company
- **Location:** Dearborn, MI, United States
- **Experience:** Experienced
- **Salary:** $85,400.0 - $192,900.0
- **Contract:** Permanent contract
- **Skills:** HTML, JavaScript (Programming Language), Artificial Intelligence, Cloud Computing, Design of User Interfaces, Python (Programming Language), Tensorflow, Next.js, Data Streaming, TypeScript, Web Application Frameworks, Spring Cloud, Pytorch, ReactJS, Large Language Models, Generative AI, Backend, Keras, Information Technology, HuggingFace, Virtual Agents
- **Published:** August 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c2c7151d5d725957

## About the Role

* Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience).
 * 2+ years building full-stack production applications (JavaScript/TypeScript, React, HTML; backend with Node.js/Next.js or Python), including 2+ years building production React/TypeScript UIs.
 * 2+ years designing and building agentic workflows with AI frameworks (LangChain, LangGraph, Google ADK) alongside Context Engineering and MCP.
 * Demonstrated experience in AI evaluation and agent/prompt observability (e.g., RAGEval, LangSmith/Langfuse, OpenTelemetry, agent trajectory/tool-use analysis).
 * 2+ years deploying cloud-native applications, with hands-on experience in GCP and Generative AI (Vertex AI)(Hard Requirement).

Even better, you may have...

 * Master's degree in Computer Science, Engineering, or a related field.
 * Applied depth in GenAI, including model/agent evaluation, prompt optimization, and NLP using PyTorch/TensorFlow/Keras and Hugging Face.
 * Experience building conversational/streaming AI user interfaces (e.g., token streaming, tool-call rendering, agent trace visualization).
 * Automotive domain experience (e.g., connected vehicle data, vehicle engineering, or manufacturing).

## Description

* Architect & Innovate: Design and deploy agentic GenAI workflows leveraging modern frameworks (LangChain, LangGraph) and Model Context Protocol (MCP) integrations.
 * Cloud & AI Operations: Deploy and operate robust AI solutions natively on GCP and Vertex AI.
 * Ensure Quality & Safety: Own LLM evaluation, prompt/agent observability, and implement strict guardrails for output quality and safety.
 * Full-Stack Development: Design and ship highly responsive frontends (React / TypeScript) that make AI outputs intuitive and actionable for engineering teams.
 * Technical Leadership: Act as a force multiplier for the team by introducing reusable AI patterns, establishing tooling, and mentoring engineers to elevate delivery efficiency across the broader group.


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