At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow's transportation.

Our Global Data Insight & Analytics organization is seeking a Senior AI Engineer to join an innovative team dedicated to transforming our code review and quality assurance processes through cutting-edge AI Agentic systems. In this role, you'll leverage your extensive experience building production-grade GenAI applications to architect and deploy autonomous AI agents that revolutionize code quality across the organization. You'll work in a collaborative environment with engineers, product managers, and business partners to deliver AI-powered solutions that provide measurable improvements to our development workflow.

Basic Qualifications

• 5+ years of professional software engineering experience with strong proficiency in Python and experience with Java

2+ years of hands-on production GenAI experience, including:

• Building and deploying AI Agentic systems or autonomous agents in production environments

• Architecting context engineering solutions including dynamic context assembly, memory systems, and agentic retrieval patterns

• Designing multi-agent orchestration and multi-hop reasoning pipelines

• Implementing agent planning strategies, tool orchestration, and human-in-the-loop patterns

• Prompt engineering, function calling, and tool-use implementations

• Proven track record of shipping production-grade AI/ML systems that delivered measurable business value

• Strong experience with agentic frameworks, particularly **ADK (Agentic Design Kit)**and Lang Graph, or similar modern agent orchestration tools

• Strong understanding of software design patterns, SOLID principles, and clean architecture applied to AI systems

• Experience with CI/CD pipelines, containerization (Docker), and cloud infrastructure

• Excellent communication skills with ability to explain complex AI concepts to both technical and non-technical audiences

• Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, or related technical field

Additional Desired Skills

• Master's degree in Computer Science, Machine Learning, AI, or related discipline

• Experience with multi-agent systems and agent orchestration frameworks beyond ADK and Lang Graph

• Hands-on experience with fine-tuning LLMs, RLHF, or custom model training

• Background in code analysis AI tools or building similar systems

• Knowledge of vector search optimization, embedding model selection, and semantic search implementations

• Experience implementing LLM guardrails, safety filters, and responsible AI practices

• Understanding of transformer architectures and attention mechanisms

• Experience with AI evaluation frameworks and establishing quality metrics for GenAI systems

• Leadership in establishing AI coding standards and architectural patterns for agent-based systems

• Passion for exploring emerging AI technologies and continuous learning in this rapidly evolving field

• Design and Deploy Production AI Agentic Systems: Architect, build, and deploy autonomous AI agents using state-of-the-art frameworks like **ADK (Agentic Design Kit)**and Lang Graph that leverage LLMs to automate code reviews, quality analysis, and development workflow optimization at scale

• Architect Advanced Context Engineering Solutions: Design sophisticated context engineering strategies including dynamic context assembly, agentic retrieval patterns, memory systems (episodic and working memory), and multi-hop reasoning pipelines for complex code analysis tasks

• Develop AI-Powered Automation Tools: Build intelligent backend services in Python and Java that leverage GenAI to detect anti-patterns, suggest improvements, and standardize development workflows

• Establish AI-Enhanced Engineering Excellence: Define and implement coding standards and best practices specifically for GenAI systems, including LLM evaluation metrics, guardrails, and responsible AI principles

• Lead AI-Powered Code Reviews: Conduct comprehensive code reviews using state-of-the-art GenAI tools and agents, providing architectural guidance on performance, security, and AI system design

• Innovate with Emerging Technologies: Stay current with rapidly evolving GenAI technologies and integrate cutting-edge capabilities (function calling, tool use, fine-tuning, prompt caching) into production systems

• Mentor on AI Engineering: Coach engineers on GenAI best practices, AI agent design patterns, prompt engineering, and responsible AI development

• Collaborate Across Functions: Partner with product owners, architects, security teams, and stakeholders to ensure AI solutions align with quality standards and business objectives