Software – Machine Lrng

Ford is seeking a talented and innovative Generative AI Engineer with a strong focus on Retrieval-Augmented Generation (RAG) systems. In this role, you will be responsible for the end-to-end design, development, and deployment of sophisticated Generative AI solutions. You will leverage various AI techniques, including vector databases, and utilize robust API development frameworks like FastAPI. A critical aspect of this role involves ensuring efficient deployment through containerization and MLOps practices, ultimately building intelligent applications that enhance user experience and automate complex processes across the organization.

• Design, develop, and deploy cutting‑edge Generative AI solutions, with a particular emphasis on Retrieval‑Augmented Generation (RAG) systems.
• Implement and optimize RAG systems, including the selection and integration of appropriate vector databases.
• Develop robust and scalable APIs using frameworks such as FastAPI to support AI‑powered applications.
• Ensure efficient and reliable deployment of AI models and applications through containerization technologies (e.g., Docker) and MLOps practices.
• Collaborate with cross‑functional teams to understand requirements and translate them into technical specifications for AI solutions.
• Apply prompt engineering techniques to effectively interact with Large Language Models (LLMs) and optimize their performance.
• Contribute to the fine‑tuning of Machine Learning models to meet specific project needs.
• Utilize Python programming and SQL for data manipulation, querying, and model development.
• Leverage Google Cloud Platform (GCP) services relevant to AI/ML for development and deployment.
• Participate in code reviews and maintain high standards of code quality and documentation using version control systems (e.g., Git).
• Stay up‑to‑date with the latest advancements in Generative AI, LLMs, and RAG technologies.

• Design, develop, and implement Generative AI models and applications, specifically focusing on building and optimizing RAG systems, including the integration and management of vector databases, using various technology stacks, with a preference for the OpenAI SDK.
• Apply fundamental Machine Learning concepts, including model fine‑tuning, to improve the performance and accuracy of AI solutions, and deploy them via efficient APIs, such as those built with FastAPI, utilizing containerization for consistent environments.
• Perform data engineering tasks to prepare, process, and manage data pipelines essential for training, evaluating, and deploying Generative AI models, including data ingestion for vector databases, ensuring data quality and accessibility.
• Utilize advanced prompt engineering techniques to optimize interactions with large language models and achieve desired outputs, and expose these capabilities through well‑designed APIs.
• Collaborate with cross‑functional teams to integrate AI solutions into existing products and services, ensuring scalability, reliability, and maintainability on cloud platforms, particularly Google Cloud Platform (GCP), adhering to MLOps principles and continuous integration/continuous deployment (CI/CD) practices.

• Bachelor’s Degree in Computer Science, Software Engineering, Artificial Intelligence, or a related quantitative field.
• 3+ years of experience in software engineering with a focus on Generative AI, Machine Learning, or related AI fields.

You may not check every box, or your experience may look a little different from what we’ve outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!

• Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related discipline.
• Experience deploying AI/ML models into production environments at scale.
• Previous experience in a large enterprise or fast‑paced technology environment.

As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new…