Data Engineer
Summary
Build and maintain scalable data pipelines and agentic AI workflows for Ford’s connected vehicles, processing telemetry and manufacturing data using cloud-native tools like GCP and AWS.
At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have an exciting opportunity for you to join our expanding area of Telemetry and Observability.
Are you enthusiastic to mine raw data and realize its hidden value by architecting and building amazing, connected data solutions that benefit our customers? Would you love to accelerate our efforts in implementing advanced agentic solutions and ML model in production?
The Senior Data Scientist role resides within the Ford’s Electric Vehicle organization. In this role, you will work on building scalable and robust data pipelines to process large volumes of manufacturing, repair, software update, call center case, and connected vehicle data to support the Ford telemetry and observability initiatives.
- Collaborate across teams and internal organizations to develop a wider data architecture plan for all of Connected Vehicle Software.
- Work with data stewards and data owners to source and understand data to ensure the best data transformation practices and processes.
- Collaborate with data analytics stakeholders to streamline the data acquisition, processing, and presentation process.
- Provide analysis of connected vehicle data to support new product developments and production vehicle improvements.
- Contribute to the design and building of a Semantic Data layer to serve AI agents.
- Work with ML Ops to 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.
- Acquire a deep understanding of vehicle engineering problems, translating them into appropriate mathematical representations and AI/ML solutions (classification, prediction, intelligent automation).
- Develop and maintain a data quality system, ensuring the overall quality and integrity of data and solutions throughout the development lifecycle, from data collection and cleaning to model deployment.
- Build data pipelines to monitoring quality of data and performance of analytical models and agentic solutions.
- Stay current on the latest practices in AI Agentic Solutions.
- Work cross-functionally and have excellent technical interpretation skills to translate technical work to a variety of stakeholders.
- Mentor more junior members of a data engineering team.
You'll have...
- Bachelor's degree in Computer Science & Engineering, related field or equivalent combination of relevant education and experience
- 3+ years of experience building full-stack applications in production environments, with proficiency in Data Science, 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)
- 1+ year of hands-on experience designing and building agentic workflows using modern AI frameworks and platforms, specifically LangChain, LangGraph, Google Agent Development Kit (ADK), and Google Vertex AI
- Experience with Context Engineering and utilizing the Model Context Protocol (MCP) to securely and seamlessly connect AI models with enterprise data sources, APIs, and development environments
- 5+ years of Python development experience, specifically for:
- Advanced data manipulation and analysis using libraries such as Pandas.
- 4+ years domain experience in the automotive industry, specifically applying AI/ML to solve complex vehicle engineering challenges, connected vehicle data, autonomous systems, or manufacturing
- Leveraging and integrating GCP 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...
- Ph.D. in Data Science or are currently enrolled in a Ph.D program, related field or equivalent combination of relevant education and experience
- 3+ years of experience in Machine Learning model development, including Natural Language Processing (NLP), utilizing deep learning frameworks (PyTorch, TensorFlow, Keras), and leveraging open-source model ecosystems like Hugging Face
- 3+ years of hands-on experience developing and deploying solutions on major cloud platforms, including Google Cloud Platform (GCP) or Amazon Web Services (AWS).
- Advanced experience with LLMOps, MLOps, and AIOps, including managing the end-to-end lifecycle, continuous evaluation, fine-tuning, and monitoring of Large Language Models in enterprise environments
- 3+ years of Front-End Development experience using modern web technologies like React, JavaScript, and HTML, to build intuitive user interfaces for AI applications.
- 2+ year experience working with DevOps tools (Docker, Kubernetes, Terraform, etc.)
- Experience with AIOps, MLops would be beneficial
- Experience implementing AI Evaluation frameworks (e.g., RAGEval) to systematically measure, benchmark, and ensure the quality, safety, and relevance of LLM outputs in production