Sr. Product Manager

Team: DevOps Engineering

Salary Range: USD 100K – USD 180K
Location: 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 We’re Looking For

• 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.

Bonus points if you also have:

• 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.

Talent shows up in a lot of different ways, and we mean that. We welcome candidates from all backgrounds and experience levels, including military members and their spouses and those without a traditional degree or tech background. If this role speaks to you, apply.

Why You’ll Like Working on Team Applied
The best work happens when smart people move fast, together. For over 40+ years, Applied has built technology that solves real problems for insurance professionals - and we’re still pushing what’s possible.

You’ll be part of our people-first culture built on trust, inclusion, and growth - where you’re supported to deliver, collaborate with teammates who care, and grow with leaders invested in your success.

How We’ll Support You

We invest in the whole person, not just the role. Our benefits and resources are built to support your health, your time, and your life outside of work:

• Medical, Dental, and Vision Coverage 
• Holiday and Vacation Time 
• Health & Wellness Days 
• A Bonus Day for Your Birthday 
•  

Compensation Transparency

Our targeted starting base salary in the United States for this position considers a variety of factors, including depth and breadth of experience, skills and role scope. Depending on the role, team members may also be eligible for additional compensation plans (bonus and commission).

Your Security Matters: 
 Our candidates’ personal information and online safety are top of mind. Applied communicates with candidates only via a secure @appliedsystems.com email address or through our official careers portal. Recruiters will never request payments or ask for financial account or sensitive personal information like Social Security numbers.

AI Utilization

We leverage AI tools to streamline parts of our recruitment workflow (such as resume parsing and interview scheduling). However, final decisions are always conducted by real humans.

EEO Statement   

Applied Systems is proud to be an Equal Employment Opportunity Employer. Diversity and Inclusion is a business imperative and is a part of building our brand and reputation. At Applied, we don’t discriminate, and we are committed to recruit, develop, retain, and promote regardless of race, religion, color, national origin, sexual orientation, gender identity, disability, age, veteran status, and other protected status as required by applicable law.  

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