AI Technical Product Owner

pitneybowes; Shelton (CT); On-site; USD 140,000 - 200,000; Full time

Job summary

Pitney Bowes is seeking a Senior / Lead Product Owner for the AI Center of Excellence in Shelton. You will own the backlog for one or more AI pods, set sprint goals aligned to quarterly outcomes, and translate ambiguous executive intent into actionable epics and acceptance criteria for engineers.

You will be hands-on with architecture considerations, define success criteria for probabilistic AI outputs, and ensure governance, observability, and cost controls are in place before deployment to

Qualifications

• Hands-on role requiring reading architecture diagrams and translating executive intent into epics, stories and acceptance criteria.
• Own and groom the backlog for AI pods, with sprint goals tied to quarterly outcomes.
• Define acceptance criteria for probabilistic AI systems, including eval sets, pass thresholds and failure modes.

Responsibilities

• Run sprint planning, standups, reviews and retrospectives for the AI pod.
• Track delivery health: velocity, cycle time, blockers, and dependencies; escalate with solutions.
• Coordinate across platform engineering, data, security, and product for planned integrations.
• Maintain governance checkpoints: model approval, data handling, human-in-the-loop, prompt and tool change control.

Skills

Backlog management

Product ownership

Agile / Scrum

Stakeholder management

Cross-functional leadership

Architecture awareness

Job description

We're hiring at Pitney Bowes, where top talent builds meaningful careers and lasting impact. We Move fast, Deliver excellence, and Win together...that's The Pitney Bowes way. Here, how we work matters just as much as what we achieve.

We're looking for people who:

• Act with urgency, accountability, and purpose
• Deliver high quality work with consistency and pride
• Collaborate effectively and elevate those around them
• Focus on outcomes that drive impact and growth

Job Description:

Function: AI Center of Excellence

Reports to: Director, AI Center of Excellence

Location: Shelton Office

Level: Senior / Lead

About the role

The AI CoE builds and ships agentic AI systems that sit on top of our core product APIs - a Copilot platform on Amazon Bedrock AgentCore, MCP-based tool servers, and AI advisors embedded in our shipping products. We are past the demo stage and into the part that is genuinely hard: making non-deterministic systems reliable, governed, measurable, and safe enough to put in front of customers and internal users.

This role owns the backlog and the delivery cadence for that work. You are the single throat to choke for what the team builds next and the reason the team can build it without friction. You will spend your day equally between defining outcomes with engineering, product, and security stakeholders - and clearing the path so the pod can actually deliver them.

This is a hands-on-the-details role, not a ceremony-running role. You will be expected to read an architecture diagram, argue about where the human-in-the-loop gate belongs, and write acceptance criteria for a system whose output is different every time you run it.

What you will own

Product ownership

• Own and groom the backlog for one or more AI pods (agent platform, AI advisor products, or AI productivity tooling).
• Set sprint goals that ladder to quarterly outcomes, not activity.
• Translate ambiguous executive intent ("we need an agent that helps SMBs ship smarter") into epics, stories, and acceptance criteria engineers can start on Monday.
• Write acceptance criteria for probabilistic systems: define what "good" means for an agent response, what the eval set is, what the pass threshold is, and what the failure mode is when it misses.
• Own the definition of done for agent capabilities - including evals, guardrails, observability, cost per interaction, and rollback path, not just "the happy path works."
• Prioritize ruthlessly across competing stakeholders: product management, engineering leadership, security, architecture, and the business units consuming the platform.
• Maintain the tool and capability catalog for the agent platform - which APIs are exposed as agent tools, which are approval-gated, which are read-only, and why.

Scrum mastery and delivery

• Run sprint planning, standup, review, and retrospective for the pod. Keep them short and keep them useful.
• Track and report delivery health: velocity, cycle time, spillover, blocked-time. Bring problems forward early rather than explaining them in hindsight.
• Remove impediments - cross-team dependencies, environment access, security review queues, vendor bottlenecks. Escalate with a proposed resolution, not just a flag.
• Facilitate estimation and scope negotiation in a domain where estimates are genuinely uncertain, without letting uncertainty become an excuse.
• Coordinate across pods and with partner teams (platform engineering, data, security, product) so integration work is planned rather than discovered.

Technical stewardship

• Partner with the architecture lead on target-state decisions and carry those decisions into the backlog with enough fidelity that they survive contact with implementation.
• Maintain requirement traceability from product requirements through to delivered agent behavior, including where a requirement was deliberately descoped and why.
• Own the AI governance checkpoints in the delivery flow: model approval, data handling review, human-in-the-loop placement, prompt and tool change control, and audit evidence.
• Keep a live view of platform economics - token spend, model selection, inference cost per use case - and treat cost regressions as defects.
• Run POCs as time-boxed experiments with a written decision at the end, not as open-ended projects.

What success looks like

First 90 days

• You know the platform architecture well enough to explain it to a VP without an engineer in the room.
• The backlog for your pod is groomed two sprints deep with acceptance criteria that engineers do not have to re-litigate in planning.
• You have identified and closed the three largest sources of delivery friction for the pod.

By six months

• Predictable delivery: sprint goals met consistently, spillover trending down, dependencies surfaced before they block.
• Every shipped agent capability has an eval set, a guardrail spec, and a cost-per-interaction number attached to it.
• Stakeholders across product, engineering, and security come to you for status rather than assembling it themselves.

Required q