Quantum Machine Learning Engineer Jobs in Alaska

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Job Responsibilities

Lead the architecture and delivery of enterprise-grade LLM and agentic AI systems that transform claims, risk, and operational workflows.

Define technical strategy for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation.

Design and implement advanced agentic systems capable of planning, reasoning, tool selection, execution, reflection, and recovery.

Architect stateful, memory-aware AI systems that manage long-running claims processes across multiple touchpoints.

Build multi-agent collaboration models that coordinate coverage analysis, document validation, fraud signals, compliance checks, and decision support.

Establish orchestration frameworks that manage task routing, context persistence, structured outputs, and failure handling.

Design secure tool integration layers connecting agents to claims systems, policy platforms, data warehouses, document repositories, and external data services.

Implement deterministic guardrails, schema validation, and output verification pipelines to reduce hallucination and execution risk.

Lead development of document intelligence systems leveraging LLMs for summarization, entity extraction, discrepancy detection, and structured data reconstruction.

Define prompt engineering standards and reusable reasoning templates for consistent, domain-aware outputs.

Oversee embedding strategies, vector indexing architecture, retrieval optimization, and knowledge grounding approaches.

Design evaluation frameworks to measure reasoning depth, workflow completion accuracy, hallucination rates, latency, and cost efficiency.

Implement observability layers that track agent decisions, tool usage, retrieval effectiveness, and drift across models and prompts.

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

Ensure compliance with Responsible AI principles, enterprise governance standards, audit requirements, and regulatory constraints.

Partner with enterprise architecture, cybersecurity, and data governance teams to define secure deployment patterns.

Mentor engineers on LLM orchestration patterns, workflow decomposition, and safe agent design.

Translate executive-level business objectives into scalable AI platform capabilities.

Lead proof-of-concepts through full production deployment with measurable ROI outcomes.

Continuously evaluate emerging foundation models, orchestration frameworks, and agent tooling for enterprise readiness.

Qualifications

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or related discipline.

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

3-5+ years designing and deploying LLM-powered systems in production environments.

Demonstrated experience architecting full agentic AI systems with planning, reflection, memory, and tool execution components.

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.

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

Experience implementing structured output enforcement, guardrails, and audit logging mechanisms.

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

Experience operating in regulated industries such as insurance, financial services, or healthcare preferred.

Proven leadership in technical design reviews, architecture governance, and cross-functional collaboration.

Strong ability to balance innovation with enterprise risk management and operational stability.

Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace.