Software Engineer, AI Enablement

Ford Ford · Auto · Dearborn, MI +2 · Global Data Insight & Analytics

Software Engineer to design and build AI-powered agents for intelligent discovery, recommendation, and management of enterprise ontology assets, requiring strong software engineering skills and practical AI experience in information discovery, recommendation, and search.

What you'd actually do

  1. Design and build AI-powered applications, agents, and intelligent workflows that improve discovery, recommendation, and management of enterprise ontology and metadata assets.
  2. Evaluate and recommend technical approaches for semantic search, knowledge management, retrieval, and recommendation challenges.
  3. Design and implement scalable backend services, APIs, and AI-enabled capabilities.
  4. Build retrieval, ranking, and recommendation systems that support intelligent user experiences.
  5. Collaborate with product managers, ontology experts, and domain stakeholders to translate business needs into technical solutions.

Skills

Required

  • Python/Java/Javascript/Angular programming languages
  • building autonomous agents using agent orchestration frameworks
  • tool integration patterns (MCP), agent communication protocols, and AI application observability
  • vector search or hybrid retrieval architectures
  • GCP services (Vertex AI, Cloud Run, and BigQuery) or similar cloud platforms
  • Strong problem-solving and system design skills
  • Ability to evaluate competing technical approaches and articulate tradeoffs
  • working with large, complex datasets and information management systems

Nice to have

  • semantic technologies, search, recommendation systems, or knowledge graphs
  • graph databases, Graph-RAG applications

What the JD emphasized

  • Experience in building autonomous agents using agent orchestration frameworks such as LangGraph/LangChain/Google ADK or Similar Technologies
  • Experience implementing tool integration patterns (MCP), agent communication protocols, and AI application observability
  • Experience with vector search or hybrid retrieval architectures

Other signals

  • AI-powered agents
  • intelligent discovery, recommendation, and management
  • semantic search, knowledge management, retrieval, and recommendation
  • scalable backend services, APIs, and AI-enabled capabilities
  • retrieval, ranking, and recommendation systems
  • technical evaluations and proof-of-concepts for emerging AI technologies
  • agent orchestration frameworks
  • vector search or hybrid retrieval architectures