Senior Staff Machine Learning Engineer, AI Agent Platform

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

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; Architect an enterprise skill ecosystem, including 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; 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, including memory hierarchies, RAG pipelines, context compaction, scratchpads, and dynamic skill/tool loading; 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; 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; 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

Requirements

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

Nice to have

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

Benefits

Personalized development programs; mentorship; certification assistance; inclusive and collaborative culture rooted in shared success; competitive pay; benefits; flexibility to support your well-being and future