Sr. Staff Engineer – AI Enablement & Engineering Excellence - Hybrid
GEICOAbout the role
At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities.
Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers’ expectations while making a real impact for our company through our shared purpose.
When you join our company, we want you to feel valued, supported and proud to work here. That’s why we offer The GEICO Pledge: Great Company, Great Culture, Great Rewards and Great Careers.
Job Description :
We are looking for a Sr. Staff Engineer to define how AI changes the way our engineering organization builds software. This is a hands-on technical leadership role for someone with deep expertise in AI-assisted development who knows how to scale it responsibly across a large engineering org and who can strengthen the engineering foundation required to do it well.
You will own our AI adoption strategy end-to-end: evaluating and selecting tooling, designing integration patterns, building internal playbooks, and working directly with engineers to embed AI into the development lifecycle in ways that produce real, measurable outcomes. Engineering excellence (standards, architecture, process) is the platform you will build to make that adoption stick.
Key Responsibilities :
AI Strategy and Adoption
Own the org-wide strategy for AI in the software development lifecycle: where to apply it, how to evaluate it, and how to scale what works.
Pilot and operationalize AI tooling across the SDLC, including AI pair programming, LLM-assisted code review, automated test generation, intelligent observability, and agentic development workflows.
Define adoption frameworks that account for productivity, code quality, security, cost, and responsible use, not just rollout logistics.
Establish metrics that measure AI's actual impact on engineering velocity, quality, and developer experience, and report findings to engineering leadership.
Bring well-reasoned tooling recommendations to engineering leadership, not just a summary of what exists in the market.
Align engineering AI adoption with the broader organizational AI journey, including approved vendors, enterprise policies, and coordination with Security and Legal.
AI System Design and Technical Depth
Provide architectural guidance for engineering teams building systems that integrate LLMs, AI agents, or ML models into production software.
Define engineering standards for AI-integrated development tooling: prompt engineering practices, evaluation frameworks, latency and cost tradeoffs, and observability for AI tools where outputs may vary.
Work hands-on with engineers on hard problems, including reviewing AI-integrated system designs, writing reference implementations, and unblocking adoption at the code level.
Engineering Excellence as an Enabler
Identify and close the engineering gaps that slow AI adoption: insufficient test coverage, brittle CI/CD pipelines, poor observability, and unclear code ownership.
Define and strengthen the engineering standards that make AI tooling more effective, treating this as a prerequisite to scaling AI well rather than a separate initiative.
Drive architectural consistency across teams, so AI-generated code and AI-assisted workflows do not introduce new forms of technical debt.
Technical Leaders
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