Role Description We are looking for an AI Engineer to help design, build, and deploy intelligent systems that power the next generation of our products. In this role, you will work at the intersection of machine learning, data, and software engineering, developing AI-driven features that deliver real value to our customers. You will collaborate closely with product, engineering, and data teams to transform ideas into scalable AI solutions—from experimentation and model development to production deployment. This is an opportunity to work with modern AI technologies and help shape how artificial intelligence is integrated across our platform. What You’ll Do - Design, develop, and deploy machine learning and AI models into production environments - Build and maintain AI-powered features and services across our products - Work with large datasets to train, evaluate, and improve model performance - Implement and optimize LLM-based applications, NLP models, or recommendation systems - Collaborate with product and engineering teams to translate business problems into AI solutions - Develop scalable pipelines for data processing, model training, and inference - Monitor model performance and continuously improve accuracy, efficiency, and reliability - Stay up to date with the latest advancements in AI, machine learning, and generative AI Qualifications - 3+ years of experience in AI, machine learning, or data science engineering - Strong programming skills in Python - Experience with machine learning frameworks such as TensorFlow, PyTorch, or similar - Familiarity with LLMs, NLP, or generative AI technologies - Experience deploying models in production environments - Knowledge of data pipelines, APIs, and cloud platforms (AWS, GCP, or Azure) - Strong problem-solving skills and ability to translate business needs into technical solutions - Excellent communication and collaboration skills

• Define and execute the enterprise AI engineering strategy aligned to Sedgwick’s claims, risk, and client service transformation goals. • Lead the architecture, development, and deployment of applied AI and agentic AI solutions across global operations. • Build and scale a high-performing AI engineering organization, including Applied AI Engineers, Agentic AI Engineers, ML Engineers, and AI Platform teams. • Establish standards for LLM integration, retrieval-augmented generation (RAG), multi-agent orchestration, workflow automation, and model lifecycle management. • Oversee the design of autonomous and semi-autonomous AI systems that support claims intake, coverage analysis, fraud detection, compliance review, and operational optimization. • Drive enterprise architecture decisions for AI platforms, including model hosting, orchestration layers, vector databases, evaluation frameworks, and observability tooling. • Ensure scalable, secure integration of AI systems with claims platforms, policy systems, document repositories, and enterprise data environments. • Define and enforce engineering best practices for prompt engineering, tool use, memory design, guardrails, structured outputs, and deterministic validation. • Establish governance frameworks for Responsible AI, explainability, auditability, and regulatory compliance. • Partner with cybersecurity, legal, compliance, and data governance teams to mitigate AI-related operational and regulatory risks. • Develop robust evaluation and benchmarking methodologies to measure reasoning quality, workflow completion rates, hallucination risk, and system reliability. • Oversee AI production operations including performance monitoring, drift detection, cost management, and service reliability. • Translate executive-level business priorities into scalable AI platform capabilities and delivery roadmaps. • Collaborate with Claims Operations, IT, Digital, and Product teams to identify high-impact AI use cases and drive measurable ROI. • Lead build-versus-buy decisions for AI tooling, foundation models, orchestration frameworks, and enterprise integrations. • Manage vendor relationships related to AI platforms, cloud providers, and model providers. • Drive adoption of AI solutions across adjusters, supervisors, and client-facing teams through strong partnership and change management alignment. • Mentor engineering leaders and establish a strong culture of technical excellence, innovation, and operational discipline. • Present AI strategy, progress, risks, and outcomes to executive leadership and board-level stakeholders. • Develop long-term AI capability roadmaps that position Sedgwick as a technology leader in claims and risk management.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role is ideal for an experienced technical leader with a passion for building scalable, production-ready AI solutions. You will drive the architecture and development of cutting-edge GenAI systems that transform how businesses analyze and act on location data. The position spans full-stack development, including backend microservices, frontend interfaces, real-time streaming pipelines, and advanced AI integrations. You will lead the deployment of LLMs, agentic workflows, and RAG architectures while ensuring responsible and ethical AI practices. Collaborating with product, data science, and engineering teams, you will define technical strategy, roadmap, and standards for next-generation business intelligence tools. The environment is fast-paced, innovative, and impact-driven, offering opportunities to influence core platform capabilities that reach enterprise customers globally. - Lead the technical vision and architecture of GenAI-powered location intelligence solutions, ensuring scalability, performance, and maintainability. - Define and execute technical roadmaps aligned with business goals and system growth requirements. - Design, implement, and deploy end-to-end AI systems, including LLM integrations, agentic workflows, and RAG pipelines. - Build and maintain data pipelines and cloud-native infrastructure to support AI/ML workloads at scale. - Oversee model deployment, fine-tuning, monitoring, and optimization for production environments. - Ensure responsible AI practices, including performance guardrails, ethical considerations, and monitoring for bias or drift. - Collaborate across cross-functional teams to translate business requirements into robust technical solutions. Qualifications - Bachelor’s degree or higher in Computer Science, Engineering, or a related field. - 10+ years of software development experience, with at least 1 year building production GenAI solutions in B2B SaaS environments. - Expert-level backend engineering skills in Python, Java, Go, or Node.js and experience designing microservices architectures on cloud platforms (AWS, GCP, Azure). - Strong frontend and full-stack proficiency, including React/Next.js, TypeScript, and real-time data streaming or WebSocket integrations. - Hands-on experience with LLM-powered applications using frameworks such as LangChain, LlamaIndex, and OpenAI/Anthropic APIs. - Expertise in agentic AI architectures, multi-step reasoning, tool calling, and memory systems for complex workflows. - Experience with RAG systems, vector databases (Pinecone, Weaviate, ChromaDB), and semantic search. - Solid data engineering foundation, including ETL, pipeline design, and infrastructure optimization for AI/ML workloads. - Proven track record taking GenAI models from prototype to production, including fine-tuning, prompt engineering, and AI guardrails. - Knowledge of MLOps practices, including model versioning, monitoring, and lifecycle management. - Strong collaboration, communication, and problem-solving skills for working with product managers, data scientists, and stakeholders. Benefits - Competitive salary with performance incentives. - Comprehensive medical, dental, and vision insurance. - Flexible remote work environment with global team collaboration. - Professional development opportunities and access to cutting-edge AI projects. - Participation in a high-growth, innovative technology environment shaping a new market. - Exposure to top-tier technical talent and mentorship opportunities.

• Refactor a single-agent prototype into a multi-agent architecture (orchestrator + specialized agents) • Design orchestration logic, agent communication, and state/session management • Implement guardrails (prompt-injection protection, output validation) • Integrate external APIs with resilient error handling • Optimize performance and latency (<2–4 seconds) • Implement monitoring, telemetry, and observability for production systems • Collaborate with cross-functional teams to ensure robust API design and integration