Fullstack AI / ML Engineer

We are seeking a passionate and experienced Full Stack AI / ML Engineer with a strong background in machine learning and a drive for building intelligent systems. As a Full-Stack AI / ML Engineer on the Ford Pro Charging team, you will design, build, and ship intelligent services that power our global EV-charging platform. If you love turning data into real-world impact and thrive on end-to-end ownership—from research notebooks to production APIs—this is your playground.

Required Skills & Qualifications :

• Experience : 2+ years of professional experience in Artificial Intelligence, Machine Learning, or Data Science roles, with a proven track record of delivering production-grade AI / ML solutions (or equivalent demonstrable expertise).
• Technical Expertise : Proficiency in Python and strong experience with core AI / ML libraries and frameworks (., TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers). Solid grasp of various machine learning algorithms (supervised, unsupervised, reinforcement learning) and deep learning architectures. Demonstrated experience applying machine learning to complex datasets, including structured and unstructured data. Proficient in API design (REST, GraphQL), microservices, and database design (SQL / NoSQL); production experience on at least one major cloud (AWS, Azure, or GCP). Practical knowledge of Docker, Kubernetes, and CI / CD pipelines (GitHub Actions, Argo, or similar).
• Problem-Solving : Excellent analytical and problem-solving skills, with proven ability to break down complex problems into iterative experiments and devise effective, scalable AI / ML solutions
• Enthusiasm & Learning : A genuine passion for technology, coupled with a self-driven commitment to continuous learning and mastery of new techniques. We value individuals who proactively identify challenges, conceptualize solutions, and lead ideation and innovation, beyond mere task execution
• Communication : Strong communication skills to articulate complex technical concepts to both technical and non-technical stakeholders.
• Education : Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, or a related quantitative field.

Bonus Points :

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