Sr. Software Engineer - Applied AI

GEICO is a leading insurance company that offers a rewarding career with endless possibilities. They are seeking a Senior Software Engineer to lead the development of AI-powered products, focusing on building scalable consumer experiences that leverage AI technology.

Responsibilities:

• Identify AI opportunities: Evaluate and prioritize opportunities to automate business processes using AI, intelligent workflows, and agent-based systems
• Design and ship applied AI solutions: Architect, build, and deploy AI solutions for high-value workflows including automation, document intelligence, decision support, and intelligent assistants
• Build agentic workflows: Design and implement AI agents and agentic workflows that orchestrate tools, APIs, reasoning steps, and business logic to automate complex processes at scale
• Own production systems: Develop services that meet high standards for scalability, resilience, performance, security, and availability
• Leverage knowledge graphs: Use knowledge graphs to enhance reasoning, entity relationships, context retrieval, and multi-step workflows
• Partner across functions: Collaborate with product, engineering, operations, and analytics partners to co-create scalable AI solutions and translate business needs into robust technical designs
• Mentor and upskill others: Coach engineers and scientists in AI, LLMs, and agentic workflow design through pairing, reviews, and architectural guidance
• Drive innovation: Explore new models, frameworks, and reasoning techniques and apply them thoughtfully to real-world challenges
• Influence architecture: Provide technical guidance on architecture, experimentation, and deployment within and across teams
• Experiment and evaluate rigorously: Run end-to-end experimentation, including hypothesis definition, measurement, validation, and iterative improvement in production environments
• Model best practices: Establish and promote engineering best practices for reliability, interpretability, safety, governance, and monitoring of production AI systems

Requirements:

• Experience: 5+ years of professional software engineering or applied machine learning experience, including 1+ years working with Generative AI or LLM-based systems in production
• Hands-on AI engineering: Strong experience with Python and modern AI frameworks such as LangChain, LangGraph, LangSmith, LlamaIndex, Hugging Face, and OpenAI or Anthropic APIs
• Production AI delivery: Demonstrated experience designing, building, and operating production AI systems, including agentic workflows and intelligent automation features
• Systems engineering excellence: Proven track record building scalable, resilient, secure, and maintainable systems that run reliably in production
• AI & data architectures: Strong understanding of agent architectures, workflow orchestration, retrieval-augmented generation, vector databases, and knowledge graph integration
• Cross-functional collaboration: Ability to work deeply with engineers, product managers, and domain experts to co-create solutions
• Mentorship: Experience mentoring engineers and helping others grow in AI, LLM, and agent-based system design
• Business impact: History of delivering measurable business outcomes with AI solutions
• Distributed systems fundamentals: Strong competency in distributed systems, service design, performance optimization, and reliability engineering
• Education: Bachelor's degree in Computer Science, Information Systems, or equivalent education or work experience
• Advanced GenAI capabilities: Experience building domain-tuned LLMs, vector reasoning techniques, or specialized retrieval architectures
• Regulated domains: Experience in insurance, financial services, or other regulated industries
• Java ecosystem: Experience deploying AI components in Java ecosystems (e.g., Spring AI, LangChain4j, Embabel)
• Deep domain modeling: Background in document intelligence, fraud or anomaly detection, or complex ontology and knowledge graph design
• AI safety & governance: Familiarity with AI safety practices, evaluation frameworks, monitoring, and regulatory compliance
• Executive communication: Ability to clearly communicate complex technical topics to senior leadership and non-technical stakeholders