Manager, Generative AI

Overview In this hands-on technical leadership role, you will guide a globally distributed engineering team to build production-grade Generative AI products. You translate business opportunities into scalable GenAI solutions and shepherd products from concept through deployment. You’ll shape the technical direction for GenAI capabilities and collaborate with stakeholders to deliver impact at scale in the insurance domain. This is a high-impact, cross-functional leadership position at Verisk that combines software engineering, AI/ML excellence, and people leadership. Compensation / BenefitsHealth InsuranceRetirement PlanDisability benefitsPaid Time Off program ResponsibilitiesLead globally distributed engineering teams with hands-on technical leadership, performance management, mentoring, and coachingOversee architecture, design, development, and delivery of production-grade GenAI products focusing on quality, security, scalability, and cost efficiencyGuide decisions on LLMs, RAG, model fine-tuning, prompt engineering, and agentic AI patternsEstablish evaluation, testing, observability, and production monitoring approaches for GenAI, including groundedness and reliability metricsEvaluate foundation models, platforms, and new technologies; make architecture and build-vs-buy decisionsPartner with Product Owners and stakeholders to translate business needs into technical requirements and delivery plansPromote engineering best practices across the GenAI lifecycle and navigate complex technical/delivery challengesCommunicate technical decisions, trade-offs, status, risks, and priorities to engineering teams and executives Key requirements7+ years in software engineering, data science, or ML with technical leadership experience2+ years hands-on experience building/delivering Generative AI/LLM solutions, preferably production applicationsProven experience architecting/operating large-scale production software, AI/ML systems, or data platformsStrong understanding of LLMs, embeddings, RAG, prompt engineering, context management, and modern GenAI architecturesStrong Python skills and experience with modern AI/ML frameworks and toolingExperience with cloud-native architectures and production deployment of scalable applicationsStrong understanding of GenAI evaluation, testing, monitoring, security, and responsible AI practicesAbility to translate business requirements into technical solutions and communicate complex decisions to engineers and executivesBachelor’s degree in Computer Science, Software Engineering, Machine Learning, Data Science, or a related technical fieldleadership and people managementclear communication with both engineers and executivescollaboration with product and business stakeholdersLLMs, embeddings, RAG, prompt engineering, context managementmodel fine-tuning/ adaptation (SFT, LoRA/PEFT, instruction tuning) preferredPython programming