Senior AI Engineer

We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world -- together. At Ford, we’re all a part of something bigger than ourselves. Are you ready to change the way the world moves?

Ford Pro is a new global business within Ford committed to commercial customer productivity. Ford Pro delivers a work-ready suite of vehicles, accessories and services for virtually every vocation, backed by technology and engineered for uptime. A true one-stop shop, we offer a full portfolio of electrified and internal combustion vehicles designed to integrate seamlessly with the Ford Pro ecosystem, helping customers' businesses thrive today and into the new era of electrification.

This job is posted as HYBRID, and requires up to 3 days a week from our Dearborn, MI office.

**Visa sponsorship is not available for this position.

As the AI Engineer, you will leverage a robust technical background and extensive experience in Data Science, MLOps, and AI/ML Engineering. Your primary responsibility will be to deliver cutting-edge analytical, machine learning, and generative AI solutions. The ideal candidate will possess strong business acumen and a deep understanding of data and AI technologies that enhance key products within Pro Tech, focusing on improving customer experiences, driving revenue growth, and increasing operational efficiency.

You will collaborate within a diverse team to develop innovative products for Ford Pro. Building healthy relationships and trust with product managers, business stakeholders, and peers across Ford Pro-Tech is essential.

- Design, develop, and deploy advanced AI models, including Generative AI, machine learning (ML), and deep learning (DL) algorithms to address complex business challenges.
- Conduct thorough data preprocessing, cleaning, and feature engineering to prepare datasets for model training and evaluation.
- Train, evaluate, and fine-tune a variety of machine learning models, ensuring optimal performance and reliability.
- Develop and maintain robust, efficient, and scalable code using Python and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Document code, experiments, and results in a clear and concise manner to facilitate knowledge sharing.
- Collaborate effectively with other engineers and stakeholders to align technical solutions with business needs.
- Work within an agile development model, closely partnering with product managers and cross-functional teams.

 

- Minimum: Master’s degree in Statistics, Computer Science, or a related field.
- Preferred: PhD in Statistics, Computer Science, or a related field.
- 4+ years of experience in developing and building cloud-based applications and deploying machine learning models.
- Strong theoretical understanding of machine learning algorithms and techniques.
- Proven hands-on experience in developing and implementing AI/ML models using Python.
- Proficiency with relevant libraries such as TensorFlow, PyTorch, and scikit-learn.
- Experience with data preprocessing, feature engineering, and model evaluation.
- Excellent problem-solving and analytical skills, with a strong ability to work independently and collaboratively.
- Exceptional communication and documentation skills.
- Specific experience with cloud computing platforms like AWS, Google Cloud, or Azure.

Technical Experience:

- Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and generative AI, such as AI Platform, BigQuery, and Dataflow.
- Strong understanding of machine learning algorithms, techniques, and frameworks, including deep learning, neural networks, and ensemble methods.
- Experience in building, training, and deploying generative AI models and machine learning solutions using tools like TensorFlow, Keras, or PyTorch.
- Familiarity with cloud-based data storage and processing technologies for efficient handling of large datasets (experience with Tekton and Terraform is a plus).
- Ability to design and implement end-to-end machine learning pipelines for data ingestion, processing, modeling, and deployment.
- Proficiency in programming languages such as Python for data manipulation, analysis, and model development.
- Experience with version control systems like GitHub for managing code repositories and facilitating collaboration.
- Understanding of containerization technologies like Docker for packaging and deploying machine learning models in production.
- Demonstrated problem-solving abilities and analytical thinking, with the capacity to communicate complex technical concepts effectively.
- Experience with generative AI technologies and practices (is a plus).
- Familiarity with softwa