Data Scientist

Ford Ford · Auto · Dearborn, MI +1 · PD Operations and Quality

Seeking an experienced Data Scientist to architect, develop, and deploy end-to-end agentic Generative AI full-stack applications for engineering challenges. This role involves designing MLOps pipelines, integrating cloud AI tools, developing front-end interfaces, and translating business needs into AI/ML solutions.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. Develop intuitive and responsive front-end user interfaces that enable seamless interaction with agentic AI systems and effectively translate complex outputs into actionable insights.
  5. Acquire a deep understanding of vehicle engineering problems, translating them into appropriate mathematical representations and AI/ML solutions (classification, prediction, intelligent automation).

Skills

Required

  • 3+ years of experience building full-stack applications in production environments
  • Proficiency in Data Science
  • JavaScript/TypeScript
  • React
  • HTML
  • backend frameworks including Next.js, Node.js, Python, or Go
  • 2+ year experience with database systems (SQL, NoSQL)
  • API design (REST/GraphQL)
  • 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+ years 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)
  • Experience implementing AI Evaluation frameworks (e.g., RAGEval)
  • 2+ year experience working with DevOps tools (Docker, Kubernetes, Terraform, etc.)
  • Leveraging and integrating GCP cloud-based AI tools like Vertex AI into production systems
  • Strong passion for evaluating, championing, and integrating new technologies and tools, particularly within GCP
  • Ability to clearly articulate and justify their business value and impact
  • Exceptional collaboration skills
  • Ability to effectively interface between technical development teams and business stakeholders
  • Comfortable working in ambiguous environments

Nice to have

  • Master's degree in Computer Science & Engineering
  • 5+ years of Python development experience
  • Advanced data manipulation and analysis using libraries such as Pandas
  • 3+ years of experience in Machine Learning model development
  • Natural Language Processing (NLP)
  • deep learning frameworks (PyTorch, TensorFlow, Keras)
  • 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
  • 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
  • Experience with AIOps, MLops
  • Domain experience in the automotive industry
  • applying AI/ML to solve complex vehicle engineering challenges
  • connected vehicle data
  • autonomous systems
  • manufacturing

What the JD emphasized

  • agentic full-stack Generative AI (GenAI) applications
  • agentic AI systems
  • agentic workflows
  • AI Evaluation frameworks

Other signals

  • Generative AI
  • Agentic applications
  • MLOps
  • Cloud deployment