Senior Machine Learning Engineer, AI Agent Platform

Sr. 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.

• 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 feed forward 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.

• 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, Postgre
  
  SQL, Mongo
  
  DB, 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.

• 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,…