AI Engineer
Ford MotorJob Description
AI Engineer
Core Technical Areas
- Agent orchestration frameworks such as Google ADK, LangChain, LangGraph, DeepAgents, AutoGen, CrewAI, or similar
- Planning and reasoning workflows
- Tool integration and function calling
- State and memory management
- Human-in-the-loop review processes
- Error handling and recovery mechanisms
- Transformer fundamentals and attention mechanisms
- Prompt engineering and structured outputs
- Context management and token optimization
- Fine-tuning and model customization
- Working with both commercial and open-source models
- Semantic and hybrid search
- Document ingestion and processing pipelines
- Embeddings and retrieval strategies
- Reranking techniques
- Vector databases such as Pinecone, Qdrant, Weaviate, pgvector, or Vertex AI Vector Search
- Knowledge access controls and source citation capabilities
- Python, FastAPI, and asynchronous programming
- REST and streaming APIs
- Event-driven and microservice architectures
- Docker and cloud deployment
- Security, monitoring, and observability
- Development acceleration using tools such as Cursor, GitHub Copilot, Claude Code, or similar
- Frontend development using React, Next.js, and TypeScript
- Agent evaluation and testing
- Observability and tracing
- Prompt security and guardrails
- Latency, quality, and cost monitoring
- Tools such as LangSmith, Phoenix, OpenTelemetry, and cloud-native monitoring platforms
- Design, build, and maintain agentic and multi-agent AI systems for complex business workflows
- Develop scalable backend services and APIs that support AI-powered applications
- Build and maintain RAG pipelines, retrieval systems, and enterprise knowledge platforms
- Implement evaluation frameworks to measure quality, reliability, safety, latency, and cost
- Establish monitoring, tracing, security, and governance practices for production AI services
- Collaborate with product, engineering, and business stakeholders to deliver impactful AI solutions
- Evaluate emerging AI models, frameworks, and tools to improve product quality and development efficiency
- Contribute to architecture decisions and engineering best practices
Required Qualifications
- 4+ years of software engineering and/or AI engineering experience
- Proven experience delivering production-grade software and AI applications
- Hands-on experience building agentic applications involving multi-step workflows, tool usage, orchestration, memory, and fault handling
- Strong proficiency in Python and FastAPI
- Experience with asynchronous programming, API development, and containerized applications
- Experience with LLM-based applications, RAG architectures, vector databases, and model evaluation
- Familiarity with agent frameworks such as Google ADK, LangChain, LangGraph, DeepAgents, AutoGen, CrewAI, or similar
- Experience deploying secure, scalable applications in cloud environments
- Strong problem-solving, communication, and collaboration skills
Preferred Qualifications
- Experience with Google Cloud Platform, Vertex AI, Gemini, and Google's agent ecosystem
- Experience with observability platforms such as LangSmith, Phoenix, or OpenTelemetry
- Experience building full-stack AI applications using React, Next.js, and TypeScript
- Knowledge of CI/CD, infrastructure automation, and modern DevOps practices
- Experience working in enterprise-scale AI environments
#LI-MF2
Experience Level
Mid LevelJob role
Job requirements
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