Staff Engineer - Applied AI

GEICO GEICO · Insurance · Bethesda, MD +1

Staff Engineer, Applied AI at GEICO to shape Generative AI for customer and associate experiences. This role involves designing, building, and scaling AI-powered capabilities, automating workflows, improving decision-making, and mentoring engineers. Focus on applied AI solutions, agentic workflows, and production deployment.

What you'd actually do

  1. Identify and evaluate opportunities for automating business processes using AI, intelligent workflows, and agent-based systems.
  2. Architect, build, and deploy applied AI solutions across high-value enterprise workflows including automation, document intelligence, decision support, and intelligent assistants.
  3. Design and implement AI agents and agentic workflows that orchestrate tools, APIs, reasoning steps, and business logic to automate complex processes at scale.
  4. Build systems and services that meet high standards for scalability, resilience, performance, and availability.
  5. Use knowledge graphs to enhance reasoning, entity relationships, context retrieval, and multi-step workflows.

Skills

Required

  • 8 or more years of professional software engineering or applied machine learning experience
  • 2 or more years working with Generative AI or LLM-based systems in production
  • Experience adding intelligence to internal processes and workflows
  • Track record of improving system reliability and scalability
  • Proven experience building scalable, resilient, secure, and maintainable products and systems
  • Strong understanding of agent architectures, workflow orchestration, retrieval-augmented generation, vector databases, and knowledge graph integration
  • Ability to collaborate deeply across teams
  • Experience mentoring engineers
  • A history of delivering measurable business outcomes from AI systems
  • Strong competency in distributed systems, service design, performance optimization, and reliability engineering

Nice to have

  • Experience building advanced Generative AI capabilities including domain-tuned LLMs, vector reasoning techniques, or specialized retrieval architectures
  • Experience with insurance, financial services, or other regulated industries
  • Experience deploying AI components in Java ecosystems including Spring AI, LangChain4j, or Embabel
  • Background in document intelligence, fraud or anomaly modeling, or complex ontology and knowledge graph design
  • Familiarity with AI safety practices, evaluation frameworks, monitoring, and regulatory compliance
  • Ability to effectively communicate complex technical topics to senior leadership and non-technical stakeholders

What the JD emphasized

  • production-ready AI systems
  • scalable, resilient, production-ready AI systems
  • production AI systems
  • production environments
  • production AI systems
  • production-grade AI deployment

Other signals

  • applied AI solutions
  • AI agents and agentic workflows
  • production-ready AI systems