We are looking for an AI Engineer to help expand the next generation of analytics and AI capabilities across Ford’s reliability, warranty, safety, and decision-support ecosystem. This role will sit at the intersection of advanced analytics, machine learning, software engineering, and applied generative AI, with a strong focus on building scalable solutions that integrate into real business processes.
In this role, you will help design, develop, and productionize AI-driven solutions that enhance existing analytics products and enable new capabilities across warranty forecasting, reliability risk analysis, engineering decision support, and intelligent research workflows. You will partner closely with data scientists, software engineers, product leads, and business stakeholders to translate complex problems into practical AI systems that improve efficiency, insight generation, and business impact.
This position is especially well suited for an engineer who enjoys applying modern AI approaches in a highly technical environment — not just building demos, but creating robust, governed, production-ready solutions that support high-value decision-making.
As an AI Engineer, you will play a critical role in leveraging data science to drive significant business impact. You will lead the development of Agentic AI applications and traditional ML models, building intelligent systems that can reason through complex scenarios while also providing predictive and prescriptive insights.
You will be responsible for architecting solutions that are not only intelligent but safe, observable, and robust. You will bridge the gap between modern Large Language Models (LLMs) and traditional statistical techniques, ensuring that AI agents operate within strict guardrails and are backed by rigorous data analysis.
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Design and build state-of-the-art AI agents using modern orchestration frameworks (e.g., LangGraph, LangChain, Google ADK) to automate complex reasoning tasks.
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Design and implement supervised and unsupervised machine learning models (e.g., regression, classification, clustering) and statistical experiments to support data-driven decision-making.
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Develop advanced RAG (Retrieval-Augmented Generation) pipelines and API connectors to ingest and synthesize data from diverse sources, including internal databases, unstructured technical documents, and external third-party data.
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Implement robust safety layers and input/output validation using specialized frameworks to prevent hallucinations, ensure data privacy, and maintain compliance.
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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.
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Adopt rigorous evaluation frameworks (e.g., Ragas, GenAI Evaluation Service) to measure performance. Stay updated on research papers and cutting-edge algorithms in both the GenAI and ML domains.
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Develop and deploy AI solutions exclusively within the Cloud Platform (GCP/AWS/Azure) ecosystem, utilizing Vertex AI, Cloud Run, and BigQuery.
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Bachelor’s or Master’s degree in a quantitative field (e.g., Computer Science, AI, Statistics, Mathematics, or Engineering). A PhD is preferred.
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3+ years of professional experience in Data Science or Software Engineering, with a strong dual focus on Generative AI/LLM applications and traditional Machine Learning.
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2+ years of hands-on experience with supervised/unsupervised learning and statistical modeling.
Technical Skills:
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Expert proficiency in Python for building autonomous agents and model interaction (e.g., LangGraph, LangChain, Google ADK).
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Advanced proficiency in Python libraries such as Scikit-learn, NumPy, Pandas, Matplotlib, TensorFlow, or PyTorch.
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Skilled in integrating diverse data sources via SQL, Vector Databases, and APIs. Experience with data augmentation and efficient loading techniques.
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Proficiency in observability frameworks, guardrail implementation, and the Model Context Protocol (MCP).
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Deep experience with GCP services, specifically Vertex AI, Cloud Run, and BigQuery for deploying scalable AI solutions.
Functional Skills:
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Ability to decompose complex business challenges into executable AI agent workflows and technical specifications.
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Excellent verbal and written communication skills, with a demonstrated ability to translate complex technical information into simple, understandable language for non-technical audiences.
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Strong skills in building relationships and collaborating effectively with stakeholders to contribute to data-driven decision-making.
