Senior Staff Machine Learning Engineer

GEICO GEICO · Insurance · Palo Alto, CA

Senior Staff AI Engineer to lead the development of GEICO's virtual agent platform, focusing on GenAI workflows for contact center employees. Requires expertise in building and architecting high-performance AI/ML platforms.

What you'd actually do

  1. Own design, architecture and development of multiple inter-connected high-performance, durable and scalable platform components that collectively power large sets of end-to-end GenAI agentic workflows. Examples include knowledge curation & management, search, prompt management, workflow orchestration, action execution systems, semantic knowledge graph, etc.
  2. Own technical decisions regarding the selection and evaluation of software technologies, tools, and frameworks, balancing build vs. buy, speed to market, future extensibility, etc.
  3. Collaborate with cross-functional teams, including data scientists, ML engineers, software engineers, product managers, designers to gather requirements, define project scope and prioritize feature backlogs. Establish pragmatic technical visions & roadmaps that balance business outcome, product release timelines and engineering excellence.
  4. Drive the strategic plan and multi-year product roadmaps of software & platform products, ensuring the efficient allocation of resources and timely delivery of solutions in conjunction of system and business dependencies.
  5. Lead teams of experience engineers for platform implementation. Troubleshoot and resolve complex software issues, ensuring optimal platform performance and reliability. Lead by example in tackling complex technical challenges and driving system-wide architectural improvements.

Skills

Required

  • architecting, designing and building multi-component AIML platform
  • utilizing and/or finetuning LLMs
  • Java
  • C++
  • Python
  • C#
  • search engine (e.g. elastic search, Qdrant)
  • data warehouse (e.g. snowflake)
  • streaming platform (e.g. Kafka)
  • relational database (e.g. postgresql)
  • Nosql (e.g. MongoDB, Cassandra)
  • distributed processing (e.g. Spark, Ray)
  • workflow management (e.g. Airflow, Temporal)
  • robotic process automation
  • context management
  • end-to-end software development life cycle (version control, CICD pipelines, Kubernetes clusters, testing, monitoring & alerting, production support etc.)
  • cloud providers such as Azure and AWS
  • conversational experiences
  • agentic workflows

Nice to have

  • Generative AI technologies

What the JD emphasized

  • minimum of 10 years of relevant experience
  • 10+ years of professional software development experience
  • 10+ years of experience architecting, designing and building multi-component AIML platform
  • 6+ years’ experience managing end-to-end software development life cycle
  • 6+ years’ experience with cloud providers
  • 3+ years’ experience utilizing and/or finetuning LLMs

Other signals

  • virtual agent platform
  • GenAI workflows
  • multi-system AI/ML platforms
  • Generative AI technologies