Distinguished Machine Learning Engineer

Key Responsibilities

• Define, own, and evolve the foundational architecture for GEICO’s Generative AI and agentic workflow platforms.
• Serve as a final technical authority on complex architectural decisions impacting multiple products, teams, and business domains.
• Design interconnected, high-performance, and durable platform components that power end-to-end GenAI workflows, including:
• Knowledge curation and management
• Search and retrieval systems
• Prompt and context management
• Workflow orchestration and action execution
• Semantic and knowledge graph systems
• Platform & GenAI Applications Strategy, Architecture, and Business Impact
• Establish and drive multi-year technical strategy and roadmaps for both AI platform capabilities and Generative AI (GenAI) applications in close partnership with product and business leaders.
• Balance speed, scalability, reliability, and extensibility while ensuring platforms and GenAI applications can support future AI use cases, evolving business needs, and organizational growth.
• Influence investment decisions by evaluating build vs. buy tradeoffs and architectural choices for both the underlying platform and GenAI applications.
• Define the reference architecture and best practices for GenAI application development, deployment, and integration, ensuring alignment with overall enterprise architecture.
• Collaborate with business stakeholders to identify high-impact GenAI application opportunities and develop strategies to maximize business value and measurable outcomes.
• Continuously assess and communicate the business impact of GenAI applications, providing clear metrics and feedback loops to inform ongoing strategy and platform evolution.
• System & Technology Evaluation
• Lead evaluation and selection of core technologies, frameworks, and infrastructure components with a emphasis on building and scaling Generative AI applications, including LLM orchestration(e.g., LangChain, LlamaIndex), agentic workflows, RAG systems, and evaluation/observability tooling, while partnering on underling AI platform infrastructure and services to support production readiness.
• Ensure architectural consistency and technical rigor across open-source, cloud-agnostic, and managed service integrations.
• Cross-Organization Influence
• Collaborate across engineering, data science, ML, product, and design organizations to align on platform and GenAI application direction, technical standards, and business objectives.
• Drive alignment across teams by translating complex technical and business concepts into clear architectural guidance and decision frameworks.
• Partner with senior technical and business leaders across departments to promote enterprise-wide adoption of GenAI best practices and maximize organizational impact.
• Technical Leadership & Problem Solving
• Tackle the most complex and ambiguous technical and business challenges affecting system-wide and application-specific performance, reliability, and scalability.
• Lead deep technical reviews, architectural assessments, and design discussions for critical AI and GenAI application initiatives.
• Guide platform and GenAI application evolution through hands-on engagement when necessary, especially in high-impact or high-risk areas.
• Mentorship & Technical Stewardship
• Mentor senior engineers and technical leads, setting a high bar for architectural thinking, engineering quality, and technical decision-making for both platform and GenAI application initiatives.
• Establish and reinforce best practices for platform and GenAI application design, reliability, observability, and operational excellence.
• Contribute to internal documentation, architectural standards, and technical knowledge sharing for both the AI platform and GenAI applications.