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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied AI Senior Manager - **Company:** American Family Insurance - **Location:** Madison, WI, United States - **Experience:** Expert - **Salary:** $150,000.0 - $255,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Application Frameworks, Cloud Computing, Code Review, Cyber Security, Information Engineering, Data Governance, Machine Learning, Software Engineering, Model Validation, Agentic-AI, AI Platforms, Machine Learning Operations, Data Pipelines - **Published:** November 4, 2026 - **Apply:** https://diversityjobs.com/career/18827271/Applied-Ai-Senior-Manager-Wisconsin-Madison ## About the Role * Demonstrated experience leading data scientists, machine learning practitioners, or applied AI specialists, including coaching, performance leadership, and talent development. * Demonstrated experience delivering machine learning, AI, or advanced analytics solutions into production business workflows with measurable outcomes. * Current hands-on technical ability, including the ability to prototype independently, read and review code, assess analytical work, and lead architecture and design conversations. * Strong understanding of AI system design, including retrieval patterns, structured output, agentic workflows, evaluation methods for non-deterministic systems, observability, and production risk. * Demonstrated ability to originate work from enterprise goals and manage a delivery portfolio under constraint, including prioritizing, sequencing, resizing, deferring, or declining work. * Experience running exploration deliberately with stated evidence and criteria for progressing from proof of concept to pilot and production. * Experience establishing operational ownership, adoption measures, support expectations, and continuous improvement practices for production solutions. * Experience managing vendor, consulting, academic, research, or contract staffing engagements, including scope, quality, knowledge transfer, handoff criteria, and ownership after an engagement ends. * Demonstrated experience partnering across Information Security, Enterprise Architecture, Governance, Risk, Legal, Privacy, Compliance, Product, Operations, Data Engineering, and Application Development. * Demonstrated ability to communicate complex technical concepts, uncertainty, tradeoffs, and recommendations to executive audiences and influence without formal authority. * Working knowledge of insurance, risk, or a comparably regulated domain, and demonstrated ability to lead through ambiguity, emerging technology, growth, and organizational change. ## Description This position leads a team within Applied AI, delivering AI/ML solutions into business workflows with clear business value. The role is accountable for a portfolio of work across business areas, assessing foundational and ideological needs for delivering AI at scale, the technical quality and operational readiness of delivered solutions, and the work environment to support and grow the people who deliver them., * Manage the development and delivery of applied AI, machine learning, and advanced analytics solutions that address high-value business problems. * Proactively offer new advancements in technology or emerging start-up capabilities to drive more comprehensive solutions with a competitive edge. * Own team-level delivery commitments, sequencing, dependencies, risks, and quality while keeping work aligned to enterprise priorities. * Ensure solutions are production-oriented, maintainable, measurable, supportable, and designed for responsible use. Manage portfolio demand and execution * Partner with the Applied AI Director, peer leaders, and business stakeholders to shape, prioritize, and sequence a cross-domain portfolio under capacity and dependency constraints by both leveraging existing solutions and finding commonalities for shared capabilities. * Translate ambiguous needs into clear problem statements, delivery plans, decision points, and measurable outcomes. Provide hands-on technical leadership * Hone technical skills and contribute to early proofs of concept, exploratory analysis, solution framing, merge request review, or other situations where hands-on leadership will accelerate learning. * Guide architecture and design decisions, review code and analytical work, and ensure technical tradeoffs are appropriate to the problem. * Set evidence-based criteria for moving work from exploration to proof of concept, pilot, production, scale, or closure. Drive value realization, adoption, and operational readiness * Define success measures with business and product owners and connect technical performance to business outcomes. * Ensure delivered capabilities have clear ownership, adoption plans, support models, escalation paths, monitoring expectations, and continuous improvement mechanisms. Embed responsible AI and evaluation discipline * Embed responsible AI practices into applied AI delivery in alignment with enterprise governance expectations. * Partner with business, governance, risk, security, privacy, legal, compliance, and platform teams to define domain-specific evaluation criteria for quality, accuracy, fairness, safety, compliance, customer experience, and business usefulness. Advance process-level AI transformation * Partner with business and technology leaders to identify, assess, and redesign high-value workflows. * Help teams connect individual AI capabilities into coherent business process journeys rather than isolated point solutions. * Clarify the changes required to human-driven processes, data, technology, and adoption to realize customer-centered value. Develop reusable AI solution patterns * Identify repeated solution patterns across domains and partner with AI Platform Engineering to convert them into reusable frameworks, components, and delivery accelerators. * Reduce one-off builds where shared approaches can improve speed, quality, governance, observability, and long-term supportability. Lead people and develop talent * Build and lead a high-performing team of data scientists and applied AI practitioners with the skills and capacity required by the enterprise portfolio, reflective of achieving the enterprise strategic goals. * Set clear priorities and expectations, coach individual contributors, develop technical and leadership capability, and communicate strategic vision. * Create a collaborative, accountable, performance-driven, and inclusive environment. Lead external delivery partnerships * Manage vendor, consulting, contract staffing, academic, and research engagements that support Applied AI delivery. * Define scope, outcomes, quality expectations, knowledge-transfer requirements, hand-off criteria, and ownership after a pilot or engagement ends. * Hold partners accountable for responsible delivery, while ensuring internal teams retain sufficient understanding and control of critical solutions. Communicate with clarity and influence * Move between business problem, technical solution, delivery trade-offs, and business outcomes; quantify impact in terms stakeholders already use. * Communicate complex technical concepts, uncertainty, risks, and recommendations to executive, business, technical, and governance audiences. * Form clear points of view, invite challenge, change position when evidence supports it, and drive decisions across organizational boundaries., * Director, Applied AI, and peer data science leaders within Applied AI for portfolio alignment, solution standards, talent strategy, shared delivery, and escalation. * Strategy, product, program, and portfolio leaders for enterprise goals, demand shaping, prioritization, operating model alignment, delivery orchestration, and value realization. * Business partners in Claims, Sales, Service & Marketing, and Product/Underwriting, including business product owners, domain experts, operations leaders, and end users. * AI Platform Engineering for MLOps capabilities, deployment paths, reusable frameworks, observability, platform standards, production support patterns, and L3 enhancement partnership. * BI Engineering & Enablement and Data Engineering for shared metrics, semantic context, data pipelines, curated data, and AI-ready data assets. * Enterprise Architecture, Information Security, Infrastructure, Cloud, Application Development, and Digital Services for architecture alignment, security review, integration, hosting, deployment, reliability, and operational support. * AI Governance, Data Governance, Model Risk, Privacy, Legal, Risk, and Compliance for responsible AI requirements, control design, evaluation expectations, approvals, and ongoing oversight. * External vendor, consulting, contract staffing, academic, and research partners for specialized expertise, delivery capacity, innovation, and knowledge transfer.