Fullstack AI/ML Engineer

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.

Design & Develop AI Solutions: Lead the design, development, training, and evaluation of machine learning models and AI solutions across various domains to enhance our products and services.

Identify AI Opportunities: Proactively identify and explore opportunities to apply data-driven solutions to improve existing products, optimize internal processes, and create new value propositions.

Model Implementation & Optimization: Implement, optimize, and deploy various machine learning algorithms and deep learning architectures to solve complex problems.

Data Management & Engineering: Collaborate with data engineers to ensure robust data collection, preprocessing, feature engineering, and pipeline development for effective model training and performance.

Backend Integration: Design and implement robust APIs and services to integrate AI/ML models and solutions seamlessly into our existing backend infrastructure, ensuring scalability, reliability, and maintainability.

Performance Monitoring & Improvement: Continuously monitor, evaluate, and fine-tune the performance, accuracy, and efficiency of deployed AI/ML models and systems.

Research & Innovation: Stay abreast of the latest advancements in AI, ML, and relevant technologies, and propose innovative solutions to push the boundaries of our product capabilities.

Testing & Deployment: Participate in the rigorous testing, deployment, and ongoing maintenance of AI/ML solutions in production environments.

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 (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers).
  • Good understanding of backend development.
  • 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:

• Domain expertise in EV charging, smart-grid, or energy-management systems.
• Experience with distributed data technologies (Spark, Flink, Kafka Streams).
• Contributions to open-source ML projects or peer-reviewed publications.
• Knowledge of ethical and responsible AI frameworks, including bias detection and model explainability.