Sr. Product Manager

Job Description

Team: DevOps Engineering

Salary Range: USD 100K – USD 180KLocation: Remote

 

Opportunity for Impact

Applied builds cloud software and AI-powered solutions that are reinventing how the global insurance industry operates, and we do it at a pace that keeps us ahead. What you ship here reaches thousands of agencies and brokers worldwide and sets the standard for what modern insurance technology looks like.

 

We’re looking for a Sr. Product Manager: AI Automation  to join our  Devops Engineering team.The IT AI Automation team hardens, deploys, runs, and partners on AI at scale. The operating model is simple: a business unit builds a proof of concept, our team hardens it, and we ship it to production on a shared, governed GCP platform with auth, CI/CD, secrets management, and monitoring. Demand already exists across CX and Finance. Homegrown apps and reconciliation tools are being shipped today with no access controls, and “what about its security?” is the first question every time. This team is the answer to that question.

 

The IT Product Manager owns the roadmap, intake, and governance for the AI Automation team. You are the front door for every business unit that wants to take an AI-powered app, automation, or agent to production. You decide what we build, what we buy, what we harden, and what we say no to, and you make sure everything that ships does so safely, with the right access controls and security posture from day one.

You translate a steady stream of business demand (Finance, Ops, Data Services, CX) into a prioritized, defensible roadmap, and you define the operating model that lets business-built POCs become durable, secure production systems.

  

What You’ll Do

• 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.

What