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GEICO

Distinguished Machine Learning Engineer

GEICO
New York, NY $210k - $350k/yr Full time Posted 15d ago
Agentic Ai Artificial Intelligence Big Data Data Architecture Data Pipeline Data Platform Data Warehouse Database Databases Enterprise Architect Feature Store Genai Machine Learning Engineer Platform Engineering Technical Lead Vector Databases
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Job Description

GEICO invites a senior engineering leader to shape enterprise-scale Generative AI and virtual agent platforms. This onsite role in New York, NY offers the opportunity to drive production-grade GenAI workflows for more than 20,000 contact center colleagues, within a culture that emphasizes collaboration, ongoing growth, and measurable impact.

Benefits and compensation

  • Great Company
  • Great Culture
  • Great Rewards
  • Great Careers
  • Competitive pay
  • Benefits
  • Flexibility to support your well-being and future

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.

Annual salary: $210,000.00 - $350,000.00

Responsibilities

  • Define, own, and evolve the foundational architecture for GEICO's Generative AI and agentic workflow platforms.
  • Act as the final technical authority for cross-product architectural decisions across multiple teams and domains.
  • Design interconnected, high-performance platform components powering end-to-end GenAI workflows.
  • Oversee knowledge curation and management.
  • Design and maintain search and retrieval systems.
  • Manage prompts and context for GenAI applications.
  • Orchestrate workflows and action execution.
  • Develop semantic and knowledge graph systems.
  • Define multi-year technical strategy and roadmaps for AI platform capabilities and GenAI applications in collaboration with product and business leaders.
  • Balance speed, scalability, reliability, and extensibility to support future AI use cases and organizational growth.
  • Influence investment decisions by evaluating build versus buy options and architectural choices.
  • Define reference architecture and best practices for GenAI app development, deployment, and integration, aligned with enterprise architecture.
  • Collaborate with business stakeholders to identify high-impact GenAI opportunities and strategies to maximize value and measurable outcomes.
  • Continuously assess and communicate GenAI business impact, with metrics and feedback loops to guide strategy.
  • Lead evaluation and selection of core technologies and infrastructure for scalable GenAI applications, including LLM orchestration (LangChain, LlamaIndex), agentic workflows, RAG systems, and observability tooling, in partnership with underlying AI platform services.
  • Collaborate across engineering, data science, ML, product, and design to align platform direction, standards, and business goals.
  • Translate complex technical and business concepts into architectural guidance to align teams.
  • Promote enterprise-wide adoption of GenAI best practices with senior leaders to maximize impact.
  • Address complex and ambiguous technical and business challenges affecting performance, reliability, and scalability.
  • Lead technical reviews and architectural assessments for major AI initiatives.
  • Engage hands-on to guide platform and GenAI evolution in high-impact or high-risk areas.
  • Mentor senior engineers and technical leads to raise architectural thinking and engineering quality.
  • Establish and reinforce best practices for design, reliability, observability, and operational excellence.
  • Contribute to documentation, architectural standards, and knowledge sharing for the AI platform and GenAI applications.

Requirements

  • Master's degree or higher in Computer Science, Engineering, Statistics, or a related field.
  • 10+ years of professional software engineering experience with deep expertise in large-scale distributed systems.
  • Extensive experience architecting multi-component AI/ML platforms using:
    • Search and retrieval systems (Elasticsearch, Qdrant, Milvus, Pinecone, Weaviate)
    • Data platforms (Snowflake, relational and NoSQL databases, feature stores)
    • Streaming and distributed processing (Kafka, Spark, Ray)
    • Workflow orchestration (Airflow, Temporal, Prefect)
    • LLM orchestration and application frameworks (LangChain, LlamaIndex)
    • Observability, evaluation, and tracing for GenAI systems (LangSmith, Arize Phoenix, Weights & Biases, OpenTelemetry)
    • Vector databases, embedding pipelines, and retrieval-augmented generation (RAG) architectures
    • Agentic frameworks and multi-agent systems for complex task execution
  • Strong background owning the full software development lifecycle, including CI/CD, Kubernetes, monitoring, and production operations.
  • Deep experience with major cloud platforms such as AWS and Azure.
  • Hands-on experience building production systems using LLMs and Generative AI technologies (e.g., GPT, Llama, Mistral, Claude) to power conversational and agentic workflows.

Technologies

  • Elasticsearch, Qdrant, Milvus, Pinecone, Weaviate
  • Snowflake, relational databases, NoSQL databases, feature stores
  • Kafka, Spark, Ray
  • Airflow, Temporal, Prefect
  • LangChain, LlamaIndex
  • LangSmith, Arize Phoenix, Weights & Biases, OpenTelemetry
  • Kubernetes, AWS, Azure
  • GPT, Llama, Mistral, Claude

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