Sr. Product Manager: AI Automation

Applied Systems builds cloud software and AI-powered solutions that are reinventing how the global insurance industry operates. They are seeking a Sr. Product Manager: AI Automation to lead the roadmap and governance for the AI Automation team, ensuring secure and effective deployment of AI applications across the organization.

Responsibilities:

• Own intake. Run a single, transparent intake process for AI app, automation, and agent requests across the company. Capture business value, users, data sensitivity, and security requirements up front so no project starts without an owner and a risk picture
• Own the roadmap. Partner with business unit leaders across Finance, Ops, Data Services, and CX to establish the prioritization and reporting mechanisms that turn competing demand into a single, defensible roadmap. Define how requests are scored against capacity, value, and risk, and how progress and trade-offs are reported back to the business, so leaders see where their work sits and why
• Own governance. Define and enforce the standards that turn a “homegrown app with no access controls” into a hardened, monitored production system. Partner with Security and Data on access controls, secrets handling, data classification, and review gates
• Own AI quality. Define eval sets and acceptance criteria for the models, assistants, and agents we ship; monitor outputs in production; own the incident response when a model gets it wrong. Quality of a non-deterministic system is a distribution, not a pass/fail
• Enforce responsible AI. Ensure bias and fairness review, explainability requirements, human-in-the-loop checkpoints, and the policy framework we need to answer enterprise procurement, audit, and (where relevant) regulator questions. Track emerging standards (NIST AI RMF, EU AI Act, sector-specific guidance) and translate them into review gates
• Define the operating model. Codify the “BU builds POC → AI Automation Team hardens → ships to production” workflow, including hand-off criteria, definition of done, and what the shared GCP platform provides versus what each team owns
• Drive buy-vs-build decisions. Run a single intake / single answer process for AI tooling evaluation and advisory (e.g., Salesforce Agentforce, competitor platforms, AI support tools) so the business gets one clear recommendation rather than fragmented opinions
• Manage stakeholders. Communicate roadmap, trade-offs, and risk to business unit leaders and the C-suite. Make the case for capacity and demonstrate the value the team delivers
• Drive adoption and enablement. Partner with business units on rollout, training, documentation, and office hours so the tools we ship get used. Adoption is a deliverable, not a hope
• Measure outcomes. Define and report on the metrics that matter, including adoption, time-to-production, security posture, cost, and business impact
• Manage AI economics. Track and forecast inference cost, vendor spend, and platform unit economics across the portfolio; make trade-offs between model choice, latency, and cost transparent so the business can fund growth without surprises

Requirements:

• 7+ years in product management, technical program management, or a closely related IT/platform role
• Demonstrated ownership of intake and prioritization for a portfolio of competing internal requests
• Working fluency in software delivery and platform concepts: CI/CD, environments, auth/identity, secrets management, monitoring, and cloud (GCP preferred; AWS/Azure acceptable)
• Practical understanding of AI/LLM application patterns (assistants, agents, retrieval, automations) and the governance questions they raise
• Familiarity with the model lifecycle and MLOps fundamentals, including eval sets, prompt and model versioning, monitoring for drift and regression, and the difference between shipping a deterministic feature and shipping a probabilistic one
• Strong grasp of security and data-governance fundamentals, including access controls, data classification, least privilege, and audit
• Working knowledge of responsible-AI frameworks and emerging regulation (NIST AI RMF, EU AI Act, model-risk management), and the practical questions they raise about bias, explainability, and human oversight
• Excellent written and verbal communication; able to align engineers, business owners, and executives
• Experience standing up or governing an internal developer/AI platform shared across business units
• Experience with enterprise-level software integrations, driven by APIs across systems such as NetSuite, Salesforce (and Agentforce), Slack, and SQL data platforms
• Background in regulated or data-sensitive environments (finance, insurance, healthcare data schemas such as Epic)
• Experience leading buy-vs-build evaluations of vendor AI tooling
• Experience running AI/SaaS vendor evaluations end-to-end with Security, Procurement, and Legal, including security reviews, SOC 2 / ISO 27001, DPAs, and model-use terms
• FinOps or cloud-cost-management experience, ideally with exposure to AI inference economics