Senior Machine Learning Engineer

GEICO GEICO · Insurance · Palo Alto, CA +1

Senior ML Engineer to design, develop, and deploy generative AI systems, focusing on agentic workflows for business value. Responsibilities include collaborating with cross-functional teams, integrating with the GEICO AI platform, and mentoring junior engineers. Requires experience in building scalable production ML applications, managing the SDLC, and working with LLM training, inferencing, and evaluation systems.

What you'd actually do

  1. Contribute to the design, development and maintenance of high-performance AI solutions that utilize agentic workflows to deliver concrete business value for internal stakeholders. Examples include knowledge assistants, voice/text-based conversational AI solutions, document/audio processing copilots, voice agents, robotic process automation, etc.
  2. Collaborate with cross-functional teams, including data scientists, ML engineers, software engineers, product managers, designers to gather requirements, help define project scope and prioritize feature backlogs. Execute pragmatic technical visions & roadmaps that balance business outcomes, product release timelines and engineering excellence.
  3. Integrate and build solutions using GEICO AI platform architecture. Partner with platform teams to communicate requirements, understand current capabilities and gaps, and contribute to platform development.
  4. Work on first-of-its-kind solutions within GEICO, with a deep understanding of business and technical processes, applications, and architecture to guide development.
  5. Participate in project planning and stakeholder management, ensuring the efficient allocation of resources and timely delivery of solutions.

Skills

Required

  • Python
  • Java
  • training, finetuning, real-time/batch inferencing and evaluation systems for AIML models and LLMs
  • end-to-end software development life cycle (e.g. CICD pipelines, Kubernetes-based deployments, testing, monitoring & alerting, production support etc.) for backend systems and APIs

Nice to have

  • Azure
  • AWS
  • eval frameworks
  • agent tooling
  • RAG pipelines
  • prompt engineering
  • interfacing directly with internal business stakeholders and/or external stakeholders on AIML initiatives

What the JD emphasized

  • 2 years in training, finetuning, real-time/batch inferencing and evaluation systems for AIML models and LLMs
  • 2 years owning end-to-end development, monitoring, maintenance, and continuous improvement of scalable, robust AIML applications.

Other signals

  • design, develop, and deploy systems that drive business value
  • agentic workflows
  • knowledge assistants, voice/text-based conversational AI solutions, document/audio processing copilots, voice agents, robotic process automation