Data Engineer

Ford’s Electric Vehicles, Digital and Design (EVDD) team is charged with delivering the company’s vision of a fully electric transportation future. EVDD is customer-obsessed, entrepreneurial, and data-driven and is dedicated to delivering industry-leading customer experience for electric vehicle buyers and owners. You’ll join an agile team of doers pioneering our EV future by working collaboratively, staying focused on only what matters, and delivering excellence day in and day out. Join us to make positive change by helping build a better world where every person is free to move and pursue their dreams.

We are looking for a data engineer with exceptional skills in Machine Learning (ML) and Artificial Intelligence (AI).

In this position... 

We are seeking an experienced data engineer with a strong focus on building and maintaining sophisticated full-stack applications. The successful candidate will possess a proven track record of designing, developing, and deploying successful innovative Generative AI (GenAI) software solutions. This role requires a proven communicator who effectively interfaces between technical and business teams, translating complex AI capabilities and technical insights into actionable business value.

What you'll do...

• Architect, develop, and deploy end-to-end agentic full-stack Generative AI (GenAI) applications that solve complex real-world problems, with a specific focus on improving engineering challenges and inefficiencies.

• Design and implement robust, scalable data science and Machine Learning Operations (MLOps) pipelines primarily within cloud environments like Google Cloud Platform (GCP) and Amazon Web Services (AWS), ensuring efficient deployment and maintenance of AI solutions.

• Lead the integration of new cloud technologies and AI tools (e.g., Vertex AI) into our workflows, continuously evaluating their potential and articulating their business value to drive innovation and efficiency.

• Develop intuitive and responsive front-end user interfaces that enable seamless interaction with agentic AI systems and effectively translate complex outputs into actionable insights.

• Acquire a deep understanding of vehicle engineering problems, translating them into appropriate mathematical representations and AI/ML solutions (classification, prediction, intelligent automation).

• Ensure the overall quality and integrity of data and solutions throughout the development lifecycle, from data collection and cleaning to model deployment.

You'll have...

• Bachelor’s Degree in Computer Science & Engineering or equivalent combination of relevant education and experience.

• 3+ years of experience building full-stack applications in production environments, with proficiency in JavaScript/TypeScript, React, HTML and backend frameworks including Next.js, Node.js, Python, or Go

• 2+ year experience with database systems (SQL, NoSQL), API design (REST/GraphQL) and building scalable API services using frameworks such as Flask or FastAPI.

• 2+ year experience deploying and maintaining applications in cloud-native environments (e.g., AWS, Azure, GCP)

• 2+ year experience working with DevOps tools (Docker, Kubernetes, Terraform, etc.)

• Leveraging and integrating cloud-based AI tools like Vertex AI into production systems.

• A strong passion for evaluating, championing, and integrating new technologies and tools, particularly within GCP, into existing workflows, with the ability to clearly articulate and justify their business value and impact.

• Exceptional collaboration skills, with the ability to effectively interface between technical development teams and business stakeholders, understanding and navigating both IT and business constraints.

• Comfortable working in ambiguous environments where problems are not always well-defined, and solutions require innovative thinking.

Even better, you may have...

• Masters Degree in Computer Science & Engineering

• 5+ years of Python development experience, specifically for: 

  • Advanced data manipulation and analysis using libraries such as Pandas.

  • Machine Learning model development, including Natural Language Processing (NLP), and utilizing deep learning frameworks like PyTorch, TensorFlow, and Keras.<