Senior Applied AI Engineer

This is a fully remote position, open to applicants in Idaho, +3 more states.

📋 Description

• Spearhead the design and delivery of enterprise-level LLM and agentic AI solutions that enhance claims, risk, and operational workflows.

• Establish a technical roadmap for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation.

• Create and implement sophisticated agentic systems that excel in planning, reasoning, tool selection, execution, reflection, and recovery.

• Architect stateful, memory-aware AI systems that oversee long-duration claims processes across various touchpoints.

• Develop multi-agent collaboration models that facilitate coverage analysis, document validation, fraud detection, compliance verification, and decision support.

• Set up orchestration frameworks that handle task routing, context retention, structured outputs, and error management.

• Design secure integration layers that connect agents to claims systems, policy platforms, data warehouses, document repositories, and external data services.

• Implement deterministic guardrails, schema validation, and output verification pipelines to mitigate hallucination and execution risks.

• Lead the creation of document intelligence systems utilizing LLMs for summarization, entity extraction, discrepancy detection, and structured data reconstruction.

• Establish prompt engineering standards and reusable reasoning templates to ensure consistent, domain-aware outputs.

• Supervise embedding strategies, vector indexing architecture, retrieval optimization, and knowledge grounding methodologies.

• Develop evaluation frameworks to assess reasoning depth, workflow completion accuracy, hallucination rates, latency, and cost efficiency.

• Create observability layers that monitor agent decisions, tool utilization, retrieval effectiveness, and model and prompt drift.

• Drive optimization strategies for token efficiency, caching, batching, and scaling inference.

• Ensure adherence to Responsible AI principles, enterprise governance standards, audit requirements, and regulatory constraints.

• Collaborate with enterprise architecture, cybersecurity, and data governance teams to outline secure deployment methodologies.

• Mentor engineers in LLM orchestration patterns, workflow decomposition, and designing safe agents.

• Convert executive-level business goals into scalable AI platform functionalities.

• Manage proof-of-concept initiatives through to full production deployment with measurable ROI results.

• Continuously assess emerging foundational models, orchestration frameworks, and agent tools for enterprise suitability.

⛳️ Requirements

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field.

• 7–10+ years of experience in AI engineering, machine learning systems, or distributed software architecture.

• 3–5+ years of experience in designing and deploying LLM-powered solutions in production settings.

• Proven experience in architecting comprehensive agentic AI systems featuring planning, reflection, memory, and tool execution components.

• Extensive knowledge of RAG architectures, embedding strategies, vector databases, and retrieval optimization.

• Significant experience in designing multi-agent orchestration frameworks and workflow engines.

• Advanced proficiency in Python and enterprise API integration methodologies.

• Experience in building secure, scalable microservices within cloud-native environments.

• Solid understanding of distributed systems, event-driven architectures, and principles of system reliability.

• Proven ability to design evaluation and benchmarking frameworks for LLM and agent reliability.

• Experience in regulated sectors such as insurance, financial services, or healthcare is preferred.

🏝️ Benefits

• Good work-life balance

• Professional development opportunities