Staff Engineer - Applied AI

GEICO•Bethesda, MD

3d•$115,000 - $230,000

About The Position

GEICO is seeking a Staff Engineer, Applied AI to help shape how Generative AI enhances customer and associate experiences across the enterprise. This is a senior, hands-on individual contributor role for someone with deep technical expertise, strong collaboration skills, and a proven ability to deliver scalable, resilient, production-ready AI systems. You will partner with engineering teams, data scientists, and product leaders to design, build, and scale AI-powered capabilities that automate workflows, improve decision-making, and elevate user experience. You will also mentor engineers who want to learn AI, LLMs, and agent-based development, fostering a culture of learning, curiosity, and innovation.

Requirements

8 or more years of professional software engineering or applied machine learning experience, including 2 or more years working with Generative AI or LLM-based systems in production.
Strong hands-on experience with Python and modern AI frameworks such as LangChain, LangGraph, LangSmith, LlamaIndex, Hugging Face, and OpenAI or Anthropic APIs.
Demonstrated experience designing, building, and operating production AI systems including agentic workflows and intelligent automation features.
Proven experience building scalable, resilient, secure, and maintainable products and systems that run reliably in production.
Strong understanding of agent architectures, workflow orchestration, retrieval-augmented generation, vector databases, and knowledge graph integration.
Ability to collaborate deeply across teams and co-create solutions with engineers, product managers, and domain experts.
Experience mentoring engineers and helping others grow in AI, LLM, and agent-based system design.
A history of delivering measurable business outcomes from AI systems.
Strong competency in distributed systems, service design, performance optimization, and reliability engineering.

Nice To Haves

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.

Responsibilities

Identify and evaluate opportunities for automating business processes using AI, intelligent workflows, and agent-based systems.
Architect, build, and deploy applied AI solutions across high-value enterprise workflows including automation, document intelligence, decision support, and intelligent assistants.
Design and implement AI agents and agentic workflows that orchestrate tools, APIs, reasoning steps, and business logic to automate complex processes at scale.
Build systems and services that meet high standards for scalability, resilience, performance, and availability.
Use knowledge graphs to enhance reasoning, entity relationships, context retrieval, and multi-step workflows.
Collaborate with product, engineering, operations, and analytics partners to co-create scalable AI solutions and translate business needs into technical designs.
Mentor engineers and scientists who want to develop AI and agentic workflow skills through coaching, pairing, reviews, and architectural guidance.
Drive innovation by exploring new models, frameworks, and reasoning techniques and applying them creatively to real-world challenges.
Lead through technical influence by providing guidance on architecture, experimentation, and deployment across multiple teams.
Run rigorous experimentation and evaluation including hypothesis definition, measurement, validation, and iterative improvement in production environments.
Establish and model engineering best practices for reliability, interpretability, safety, governance, and monitoring of production AI systems.