Staff Engineer, Ford Pro Intelligence

In this role...

We are looking for a visionary Staff Engineer to serve as the technical North Star for our data and AI-driven platforms. In this role, you will be responsible for the evolution of our core architecture, moving us toward a future where real-time data processing and AI are seamlessly integrated into every facet of our product.

As our data ecosystem grows in complexity, we need a leader who can navigate the intersection of high-performance backend engineering and modern AI capabilities. You will tackle "unsolved" problems—designing systems that handle petabytes of data with millisecond latency, architecting multi-tenant API ecosystems, and building the infrastructure that delights our customers who are leveraging our AI solutions for their everyday work. You are a "leader of leaders," someone who influences our multi-year technical roadmap and ensures that our engineering standards remain world-class while we innovate at speed on the Google Cloud Platform (GCP).

• Languages:
  
  Expert-level proficiency in Java and Python. Strong experience with Kotlin for modern backend services.
• Cloud Platform:
  Deep experience with GCP (Google Cloud Platform), specifically Big Query, Dataflow, Vertex AI, GKE (Google Kubernetes Engine), and IAM.
• Data Engineering:
  Mastery of SQL and data modeling. Proven track record of building real-time processing systems at scale and robust batch ETL/ELT pipelines.
• AI Engineering:
  Practical experience deploying AI/ML models in production, prompt engineering, fine-tuning, and working with vector databases (e.g., Pinecone, Weaviate, or Vertex AI Search).
• System Design:
  Expert knowledge of distributed systems, CAP theorem, microservices patterns, and event-driven architecture.

• Strategic Thinking:
  Ability to look 12–24 months ahead and identify technical debt or opportunities for innovation.
• Communication:
  Ability to explain complex technical concepts to non-technical stakeholders and executives.
• Execution: A "get it done" attitude with the ability to navigate ambiguity and drive projects to completion in a fast-paced environment.

• Architectural Leadership:
  Design and oversee the implementation of highly scalable, distributed backend systems and microservices using Java, Kotlin, and Python.
• Data Strategy:
  Define the data architecture and modeling standards for both relational (SQL) and non-relational systems, ensuring data integrity, security, and high availability.
• Streaming & Batch Processing:
  Lead the design of real-time data pipelines (e.g., using Dataflow, Pub/Sub, or Kafka) and batch processing frameworks to handle petabyte-scale data efficiently.
• AI Integration:
  Drive the AI-first engineering culture by integrating LLMs, machine learning models, and RAG (Retrieval-Augmented Generation) patterns into production workflows using GCP Vertex AI.
• Cloud Excellence:
  Optimize GCP infrastructure for performance and cost, leveraging GKE, Big Query, and Cloud Spanner to support global-scale operations.
• API & Ecosystem Design:
  Set the standard for API development (REST, GraphQL, gRPC), ensuring seamless integration across internal services and external partners.
• Technical Governance:
  Conduct architecture reviews, define CI/CD best practices, and ensure the team maintains a high bar for code quality, testing, and observability.
• Mentorship:
  Act as a force multiplier by mentoring Senior Engineers, fostering a culture of continuous learning and technical excellence.

• Bachelor’s Degree in Computer Science, Data Science, or a related field
• 10+ years of professional software engineering experience, with at least 5 years using Python and SQL
• 5 years experience building and consuming APIs to drive complex data integrations across distributed systems
• 5 years experience migrating or building large-scale architectures on GCP
• 4+ years in a Senior or Staff capacity, overseeing large-scale distributed systems
• 2+ years practical experience using LLM APIs or building GenAI‑enabled applications
• Experience in a "Product-led" engineering environment where you have directly influenced product features through technical capability