Context Engineer Jobs (NOW HIRING)

Why Join GEICO?

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 on relentless innovation to exceed our customers' expectations while making a real impact on local communities nationwide.

Founded in 1936, GEICO is a member of the Berkshire Hathaway family of companies and one of the largest auto insurers in the United States. 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.

Sr. Staff Machine Learning Engineer - AI Agent Platform

Position Description

GEICO is seeking an exceptional Sr. Staff ML Engineer to join our AI organization. You will serve as a technical leader and key architect for GEICO's virtual assistant platform that elevates productivity for 30K+ internal associates and the customer experience for millions of policyholders.

Sr. Staff AI Agent Platform Engineers set the technical vision and drive the architecture of multi-tenant services that power the building, testing, deployment, and hosting of LLM-based AI agents. This includes multi-agent orchestration, standardized interoperability protocols (MCP, A2A), AI agent skill ecosystems with marketplace and governance capabilities, production-grade harness & context engineering, and guardrail frameworks for safe autonomous operation at enterprise scale.

Responsibilities

• Technical Vision & Architecture: Define the long-term technical strategy for GEICO's AI agent platform - including multi-agent orchestration, AI agent lifecycle management, evaluation frameworks, skill registries and marketplace, and workflow orchestration.
• AI Agent Skills & Marketplace: Architect an enterprise skill ecosystem - reusable capability packages that encode domain expertise and workflows into portable, discoverable modules. Build and govern an internal skill marketplace with versioning, security vetting, approval workflows, progressive disclosure loading, and usage analytics.
• Harness & Context Engineering: Lead design of production-grade AI agent harnesses (tool dispatch, context management, error recovery, session state, fine-grained Authn/AuthZ) that makes AI agents reliable for long-running workflows. Apply feedforward guides (linters, architecture constraints, spec-driven validation) and feedback sensors (test execution, LLM-as-judge) mixing computational and inferential controls. Design context engineering systems that treat the LLM context window as a managed resource - memory hierarchies, RAG pipelines, context compaction, scratchpads, and dynamic skill/tool loading.
• Platform & Interoperability: Own high-performance platform components powering end-to-end agentic workflows: MCP server/registry management, A2A communication infrastructure, prompt management, workflow orchestration, guardrail enforcement, and observability pipelines.
• AI Safety & Governance: Establish AI agent governance frameworks including bounded autonomy, human-in-the-loop escalation, audit trails, prompt guardrails, and RBAC/ABAC access controls. Extend governance to skill-level security - vetting published skills for hidden payloads, injection vectors, and data exfiltration risks.
• Leadership: Collaborate cross-functionally with data scientists, engineers, product managers, and designers. Mentor engineers at all levels. Elevate AI engineering best practices - including harness engineering patterns and agentic coding tools - across the company.

Basic Qualifications

• 8+ years of professional software development experience with at least two languages (Java, C++, Python, Go, or C#).
• 6+ years designing and building AI/ML platforms using open-source/cloud-agnostic components (Elasticsearch, Qdrant, Kafka, PostgreSQL, MongoDB, Spark, Ray, Temporal, Redis, Neo4j, etc.).
• 5+ years managing end-to-end SDLCs (CI/CD, Kubernetes, testing, monitoring, production support).
• 4+ years building training, fine-tuning, and inferencing systems for LLMs, especially on GPU infrastructure.
• 3+ years designing and operating multi-agent or agentic AI systems in production.
• Strong understanding of context engineering - memory architectures, RAG, context compaction, and dynamic information management for LLMs.
• Demonstrated track record leading technical initiatives, setting architectural direction, and mentoring across teams.
• Bachelor's degree in CS, Engineering, or related field; advanced degree highly desirable.

Preferred Qualifications

• 6+ years with cloud providers (Azure, AWS), including container orchestration and GPU compute.
• 3+ years building agentic workflows with open-source and proprietary LLMs (Llama, Qwen, Claude, Gpt, etc.).
• Hands-on experience with MCP and A2A protocols - MCP server development, AI agent card discovery, task delegation patterns.
• Experience with harness engineering. (tool dispatch, error recovery, session state, sub-agent coordination, planning & reasoning)
• Experience designing AI agent skill systems: building and governing reusable skill packages, skill marketplaces with discovery, versioning, security vetting, and progressive disclosure.
• Experience with context engineering at scale: memory hierarchies, RAG optimization, compaction/summarization, state isolation, etc.
• Experience with multi-agent orchestration frameworks (LangGraph, AutoGen, CrewAI).
• Experience with LLM observability & evaluation platforms (LangSmith, Arize Phoenix, Langfuse).
• Experience building guardrail systems (prompt injection defense, PII detection, skill-level security auditing).
• Understanding of AI safety, model governance, and regulatory compliance in regulated industries.

If you are passionate about pushing the boundaries of generative AI platforms, thrive in a hands-on technical leadership role, and enjoy solving complex, large-scale problems, we encourage you to apply!

Annual Salary

The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate's work experience, education and training, the work location as well as market and business considerations.

The GEICO Pledge:

Great Company:Protecting customers through life's twists and turns with innovation and integrity.

Great Careers:Personalized development programs, mentorship, and certification assistance.

Great Culture:Inclusive and collaborative culture rooted in shared success.

Great Rewards:Competitive pay, benefits, and flexibility to support your well-being and future.

The equal employment opportunity policy of the GEICO Companies provides for a fair and equal employment opportunity for all associates and job applicants regardless of race, color, religious creed, national origin, ancestry, age, gender, pregnancy, sexual orientation, gender identity, marital status, familial status, disability or genetic information, in compliance with applicable federal, state and local law. GEICO hires and promotes individuals solely on the basis of their qualifications for the job to be filled.

GEICO reasonably accommodates qualified individuals with disabilities to enable them to receive equal employment opportunity and/or perform the essential functions of the job, unless the accommodation would impose an undue hardship to the Company. This applies to all applicants and associates. GEICO also provides a work environment in which each associate is able to be productive and work to the best of their ability. We do not condone or tolerate an atmosphere of intimidation or harassment. We expect and require the cooperation of all associates in maintaining an atmosphere free from discrimination and harassment with mutual respect by and for all associates and applicants.