Senior Staff Engineer - Java / Test Engineering & Applied AI - Hybrid

GEICO•Bethesda, MD

1d•Hybrid

About The Position

GEICO is seeking a Senior Staff Engineer to innovate and build new systems, improve existing ones, and apply knowledge to solve critical problems. This role involves setting technical direction for a significant product or platform capability area, leading a multi-team technical roadmap to increase delivery velocity and unlock new engineering capabilities, and shaping the incorporation of applied AI and intelligent automation into enterprise solutions to improve business outcomes. The ideal candidate will define reusable frameworks and standards, drive adoption, and communicate technical strategy effectively to both technical and business stakeholders. This position requires a self-directed individual with deep technical expertise, strong judgment in applying AI technologies, and a proven track record of enhancing engineering quality, including automated testing and AI-accelerated test practices, while ensuring solutions meet enterprise standards for reliability, security, and operability.

Requirements

Extensive experience architecting and building large-scale SaaS platforms and microservices (GraphQL, gRPC, Java, Python, Kafka) with API and event-driven patterns, deployed on Docker/Kubernetes and major cloud providers (AWS, GCP, Azure).
2+ years shipping LLM, generative AI, or intelligent automation in production, with strong judgment on when to use agents, RAG, classical ML, rules-based workflows, or hybrid approaches; experience integrating AI into enterprise services and designing durable, observable workflows (e.g., Temporal, Fable).
Deep hands-on expertise across the full lifecycle development including test engineering practices and automated testing, shadow validation and test data management practices.
Deep automated testing practice across the test pyramid, with proven use of AI to accelerate authoring, maintenance, and failure analysis; governance for non-deterministic tooling; and production engineering discipline across security (OAuth, SAML, Active Directory), observability, and operating AI-augmented systems—including evaluation, regression testing, safety, and governance.
Self-directed Senior Staff technical leadership: ownership of multi-team product/platform direction; reusable frameworks and standards; driving adoption and buy-in; mentoring Staff and Senior engineers into leaders; crisp communication to stakeholders; and transferring applied AI and platform capabilities from prototype to production.
Experience 10+ years full-stack development experience (Java/C#/Python/Go), with expertise in client-side and server-side frameworks.
6+ years of experience with architecture and technical direction across multiple teams.
2+ years of applied AI, generative AI, or intelligent automation experience in production, or demonstrable ownership of AI-enabled platform capabilities.
Significant ownership of automated testing strategy, with evidence of AI-assisted testing or test-platform acceleration preferred.
4+ years of experience with AWS, GCP, Azure, or another cloud service.
Bachelor’s degree in Computer Science, Information Systems, or equivalent education or work experience.

Responsibilities

Own technical architecture and direction for a medium-to-large, cross-functional product or platform capability area; align roadmaps across teams and surface tradeoffs to stakeholders, including applied AI architecture, technology selection, and integration patterns.
Drive adoption of platforms, patterns, and standards—articulate a clear technical vision, build organizational buy-in, and measure uptake across teams.
Collaborate across the tech organization to solve our toughest problems, working with product, domain experts, and analytics partners to translate business needs into technical designs that may include AI components.
Deliver high-quality services and software for a variety of domains, including AI-augmented workflows and intelligent services where appropriate, with accountability for the quality, usability, and performance of the solutions included overseeing how we apply testing and ensure quality to our deliverables.
Identify and evaluate opportunities to apply AI, intelligent automation, and agentic workflows to enterprise processes, distinguishing high-value use cases from hype-driven experiments.
Define and advance the automated testing strategy for services and platforms across the test pyramid (unit, contract, integration, E2E, performance, and related practices)—including shift-left quality, CI gates, flaky-test reduction, and clear test ownership models and system integrator management.
Accelerate test creation, maintenance, and triage with AI (e.g., AI-assisted test generation, coverage gap analysis, failure clustering, root-cause suggestions, synthetic data, visual/regression assist), with human review, determinism, and governance so AI does not introduce brittle or insecure tests.
Utilize programming languages like Java, C#, or other object-oriented languages, SQL, and NoSQL databases, container orchestration services including Docker and Kubernetes, cloud tools and services across Azure/AWS/GCP, and integrate AI services, model APIs, and orchestration tooling into the platform stack.
Multiply leadership capacity: coach Staff and Senior engineers on architecture, decision-making, and stakeholder communication; sponsor communities of practice; and strengthen applied AI patterns, production evaluation, and responsible deployment practices across the engineering community.
Translate technical strategy for tech and business VPs at the right altitude.
Lead department-oriented continuous improvement; participate in communities of practice and industry forums; and sponsor continuous learning that raises technical and non-technical leadership skills across the organization.