Staff Engineer, Ford Pro Intelligence

Ford Ford · Auto · United States · PD Operations and Quality

Staff Engineer role focused on architecting and evolving data and AI-driven platforms, integrating real-time data processing and AI into products. Responsibilities include designing scalable backend systems, defining data architecture, leading real-time data pipelines, integrating LLMs and RAG patterns using GCP Vertex AI, optimizing GCP infrastructure, setting API standards, and providing technical governance and mentorship. Requires expertise in Java, Python, GCP, data engineering, AI engineering (prompt engineering, fine-tuning, vector databases), and distributed systems.

What you'd actually do

  1. Design and oversee the implementation of highly scalable, distributed backend systems and microservices using Java, Kotlin, and Python.
  2. Define the data architecture and modeling standards for both relational (SQL) and non-relational systems, ensuring data integrity, security, and high availability.
  3. 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.
  4. 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.
  5. Optimize GCP infrastructure for performance and cost, leveraging GKE, BigQuery, and Cloud Spanner to support global-scale operations.

Skills

Required

  • Java
  • Python
  • Kotlin
  • GCP
  • BigQuery
  • Dataflow
  • Vertex AI
  • GKE
  • IAM
  • SQL
  • Data modeling
  • Real-time processing systems
  • Batch ETL/ELT pipelines
  • Prompt engineering
  • Fine-tuning
  • Vector databases
  • Distributed systems
  • CAP theorem
  • Microservices patterns
  • Event-driven architecture
  • REST
  • GraphQL
  • gRPC
  • CI/CD best practices
  • Code quality
  • Testing
  • Observability
  • LLM APIs
  • GenAI-enabled applications

Nice to have

  • MLOps frameworks (Kubeflow, MLflow)
  • Contributions to Open Source projects
  • Recognized presence in the tech community (talks, blogs, etc.)

What the JD emphasized

  • Expert-level proficiency in Java and Python
  • Deep experience with GCP (Google Cloud Platform)
  • Mastery of SQL and data modeling
  • Practical experience deploying AI/ML models in production
  • Expert knowledge of distributed systems
  • 2+ years Practical experience using LLM APIs or building GenAI-enabled applications

Other signals

  • Integrating LLMs, machine learning models, and RAG into production workflows
  • Deploying AI/ML models in production
  • Building the infrastructure that delights our customers who are leveraging our AI solutions