AI Engineer

Ford/GDIA Mission and Scope:

At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow’s transportation.

Creating the future of smart mobility requires the highly intelligent use of data, metrics, and analytics. That’s where you can make an impact as part of our Global Data Insight & Analytics team. We are the trusted advisers that enable Ford to clearly see business conditions, customer needs, and the competitive landscape. With our support, key decision-makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision-making.

The Global Data Insights and Analytics (GDI&A) department at Ford Motors Company is looking for qualified people who can develop scalable solutions to complex real world problems using Generative AI, Agentic Workflows, Machine Learning, and Statistics. The goal of GDI&A is to drive evidence-based decision making by providing insights from data. Applications for GDI&A include, but are not limited to, Connected Vehicle, Smart Mobility, Advanced Operations, Manufacturing, Supply chain, Logistics, and Warranty Analytics.

About the Role:

You will be part of the FCSD analytics team, playing a critical role in leveraging data science to drive significant business impact within Ford Customer Service Division. As an AI Engineer, you will lead the development of Agentic AI applications. You will build intelligent systems that can reason through complex service scenarios by synthesizing information from a vast array of sources including internal databases, technical/policy documents, APIs, and external third party data to make the lives of service advisors easier and more efficient.

You will be responsible for architecting solutions that are not only intelligent but safe, observable, and discoverable. You will ensure that these agents operate within strict guardrails, log their reasoning steps for auditability, and integrate seamlessly with Ford’s internal ecosystem (and future external partners) via standardized protocols.

Job Responsibilities:

• Agentic System Architecture: Design and build state of the art AI agents using modern orchestration frameworks (e.g., LangGraph, LangChain, Google ADK)
• Multi Source Data Integration: Develop advanced RAG pipelines and API connectors to ingest and synthesize data from diverse sources, including internal databases, unstructured technical/policy documents, internal APIs, and external sources. 
• Safety & Guardrails: Implement robust safety layers and input/output validation using specialized frameworks (e.g., Google Model Armor) to prevent hallucinations, ensure data privacy, and maintain compliance with Ford guidelines. 
• Observability & Tracing: Build comprehensive monitoring pipelines using advanced evaluation tools (e.g., Arize Phoenix, Langfuse) to trace agent reasoning steps, track token usage, and monitor latency in production. 
• Global Discoverability: Champion reusability by registering developed tools as MCP servers making agent capabilities accessible to consumers enterprise wide.  
• Continuous Improvement: Adopt rigorous evaluation frameworks (e.g., Ragas, GenAI Evaluation Service) to measure performance and iterate on agent accuracy. 
• GCP Native Development: Develop and deploy AI solutions exclusively within the GCP ecosystem, utilizing Vertex AI, Cloud Run, and BigQuery.

Qualifications:

• 3+ years of professional experience in Data Science or Software Engineering, with a recent, strong focus on Generative AI and LLM applications.

• Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, AI, Statistics, Mathematics). 

• Deep understanding of the GCP stack for AI/Data.

• Proven experience building Agentic workflows (reasoning loops, tool use/function calling) versus simple chatbots. 

• Experience implementing LLM Observability, Safety Guardrails, and Evaluation pipelines in a professional environment.

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Technical Skills:

• Agentic AI & Frameworks: Expert proficiency in Python for building autonomous agents, orchestration, and model interaction (e.g., LangGraph, LangChain, Google ADK).

• Cloud Infrastructure: Deep experience with GCP services, specifically Vertex AI, Cloud Run, and BigQuery for deploying scalable AI solutions. 

• Data Engineering: Skilled in integrating diverse data sources via SQL, Vector Databases, and APIs for external tools. 

• MLOps, Safety & Evaluation: Proficiency in observability and guardrail frameworks, and the Model Context Protocol.

Functional Skills:

• Ability to decompose complex business challenges into executable AI agent workflows and technical specifications.
• Excellent verbal and written communication skills, with a demonstrated ability to translate complex technical information and results into simple, understandable language for non technical audiences.
• Strong business engagement skills, including the ability to build relationships, collaborate effectively with stakeholders, and contribute to data driven decision making.