2 months ago
Remote, United StatesSenior
Responsibilities
- Lead the architecture and delivery of enterprise-grade LLM and agentic AI systems for claims, risk, and operational workflows.
- Define strategies for RAG, multi-agent orchestration, autonomous workflow automation, embeddings, vector indexing, retrieval optimization, and knowledge grounding.
- Design stateful, memory-aware, multi-agent systems with planning, reasoning, tool selection, execution, reflection, recovery, and workflow coordination.
- Build secure integrations with claims systems, policy platforms, data warehouses, document repositories, and external data services.
- Implement guardrails, schema validation, output verification, evaluation frameworks, observability, audit logging, and reliability measurement.
- Lead LLM-based document intelligence for summarization, entity extraction, discrepancy detection, and structured data reconstruction.
- Optimize token usage, caching, batching, inference scaling, latency, cost, and workflow completion accuracy.
- Ensure compliance with responsible AI principles, enterprise governance, audit requirements, security standards, and regulatory constraints.
- Mentor engineers and lead technical design reviews, architecture governance, proof-of-concepts, and production deployments with measurable ROI.
- Evaluate emerging foundation models, orchestration frameworks, and agent tooling for enterprise readiness.
Requirements
- Bachelor's or master's degree in Computer Science, Artificial Intelligence, Engineering, or a related discipline.
- 7–10+ years of experience in AI engineering, machine learning systems, or distributed software architecture.
- 3–5+ years of experience designing and deploying LLM-powered systems in production.
- Demonstrated experience architecting full agentic AI systems with planning, reflection, memory, and tool execution.
- Deep expertise in RAG architectures, embedding strategies, vector databases, and retrieval optimization.
- Strong experience designing multi-agent orchestration frameworks and workflow engines.
- Advanced proficiency in Python and enterprise API integration patterns.
- Experience building secure, scalable microservices in cloud-native environments and understanding distributed systems, event-driven architectures, and system reliability principles.
- Experience implementing structured output enforcement, guardrails, audit logging, and LLM or agent evaluation frameworks.
- Experience in regulated industries such as insurance, financial services, or healthcare is preferred.
- Proven leadership in technical design reviews, architecture governance, cross-functional collaboration, and balancing innovation with enterprise risk management.
Benefits
- Meaningful work serving people facing unexpected claims and other challenges.
- Work-life balance and a caring, inclusive workplace culture.
- Opportunity to grow within a large global organization.
- Equal opportunity and drug-free workplace.
Tech Stack
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