AI Engineer

Job Description

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?

Do you believe data tells the real story? We do! Redefining mobility requires quality data, metrics, and analytics, as well as insightful interpreters and analysts. That's where Global Data Insight & Analytics makes an impact. We advise leadership on business conditions, customer needs, and the competitive landscape. With our support, key decision makers can act in meaningful, positive ways. Join us and use your data expertise and analytics skills to drive evidence-based, timely decision making.

We are seeking an experienced individual to drive critical development activities within FCSD, focusing on hands-on solution design, model development, and deployment. As an AI Engineer, you should have a strong technical background and demonstrate experience in Data Science, MLOps, and AI/ML Engineering. This role involves implementing analytical and machine learning solutions that ensure robustness, scalability, and compliance while following software engineering best practices.

Responsibilities include the end-to-end development of AI products and services, from concept to production, working within mixed-skill teams and in close partnership with Product Managers and business stakeholders. By leveraging technical expertise and business acumen, you will deliver impactful AI solutions that improve customer experience, increase revenue, and drive business efficiency at scale.

Responsibilities

• Design, develop, and implement AI/ML models and algorithms to solve complex problems
• Perform data preprocessing, cleaning, and feature engineering to prepare data for model training
• Train, evaluate, and tune various machine learning models
• Develop and maintain robust and efficient code using Python and relevant libraries (e.g., Tensor Flow, PyTorch, scikit-learn)
• Document code, experiments, and results clearly and concisely
• Collaborate effectively with other engineers within the team
• Ability to thrive in an agile development environment, collaborating closely with product managers and cross-functional engineers

Qualifications

We recognize that no one person will embody every single quality or skill listed below. If you are passionate about AI and have a strong foundation in machine learning and software engineering, we encourage you to apply.

Education:

• Bachelor's Degree in Data Science, Predictive Analytics, Statistics, Applied Mathematics, Physics, AI, Computer Science, or a related quantitative field
• Master's Degree in a related field is preferred

Experience:

• 3+ years of experience building and implementing models using AI/ML frameworks and libraries to solve practical business problems and deliver measurable impact
• Experience with the full machine learning lifecycle, including data preprocessing, feature engineering, and model evaluation
• Demonstrated problem-solving skills, analytical thinking, and the ability to communicate complex technical concepts effectively
• Ability to work independently and as part of a team

Required Technical Experience:

• Deep experience using open-source data science technologies such as Python and SQL for data manipulation, analysis, and model development
• Experience using Gen AI technologies
• Strong understanding of machine learning algorithms, techniques, and frameworks
• Proficiency in building and training models using frameworks such as Tensor Flow, PyTorch, or Scikit-Learn
• Ability to design and implement end-to-end machine learning pipelines for data ingestion, processing, modeling, and deployment
• Experience deploying ML solutions on cloud platforms (AWS, GCP, Azure), including familiarity with cloud-based storage and processing
• Experience using version control systems like Git Hub for managing code repositories and collaboration
• Understanding of containerization technologies like Docker for packaging machine learning models and deploying them in production
• Experience in Software Engineering practices such as CI/CD, unit testing, and code reviews

Preferred Experience:

• Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as Vertex AI, Big Query, and Dataflow
• Experience building and deploying cloud-native Python applications
• Experience with common Agentic AI Frameworks
• Experience handling large datasets efficiently using Spark or other tools
• Familiarity with Tekton or Terraform for cloud-native automation
• Experience developing algorithmic pricing engines or optimization models
• Experience implementing Retrieval-Augmented Generation (RAG) and semantic search architectures
• Experience transforming semi-structured machine data or event logs into actionable features for diagnostic, root cause, or predictive models

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…