Senior Applied AI Engineer

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🕒 Junho 11

🥔 Idaho, Louisiana, +2 estados a mais – Remoto

🥔 Idaho – Remote ⚜️ Louisiana – Remote 🌾 Nebraska – Remote 🎸 Tennessee – Remote

⏰ Tempo Integral

🟠 Sênior

🤖 Engenheiro de IA

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Sedgwick

SiteLinkedInTodas as Vagas

10.000+ funcionários

🏗️ Construção

💼 Consultoria

🏥 Saúde

Construction • Consulting • Healthcare

A Sedgwick é uma fornecedora global de soluções de tecnologia para riscos, benefícios e negócios integrados. Eles ajudam pessoas e organizações a gerenciar e mitigar riscos com soluções em administração de sinistros de acidentes, saúde, invalidez, compensação por desemprego e responsabilidade, entre outros. A Sedgwick oferece serviços como administração de sinistros, consultoria de construção, contabilidade forense e engenharia forense. Suas especialidades incluem restauração de propriedades, proteção de marcas e prevenção de perdas em vários setores, incluindo agricultura, construção e setores ambientais. A empresa enfatiza diversidade, equidade e inclusão (DEI) e práticas ambientais, sociais e de governança (ESG).

Descrição

• 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.

🎯 Requisitos

• 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. • 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.

🏖️ Benefícios

• Good work-life balance • Professional development opportunities