Senior Applied AI Product Manager

Organization OverviewCompany Description

QAD is a leading provider of ERP solutions purpose-built for manufacturing industries — automotive, life sciences, food & beverage, high tech, and industrial. Serving thousands of global manufacturers, QAD's Adaptive Manufacturing Cloud helps companies operate with greater precision, agility, and intelligence.

Enterprise software is entering a third era. The first gave manufacturers a System of Record — ERP that answered 'what do we have and what did we commit to?' The second gave them Data Infrastructure — the ability to move, analyse, and query that data at scale. The third era is domain-specific intelligence: AI agents that can act autonomously on manufacturing data, but only if that data has been given the context, relationships, and governed rules that allow an agent to reason correctly.

ERA — QAD's Enterprise Resource Allocation platform — is the domain intelligence layer that sits between any ERP and any AI agent. It encodes what manufacturing data means, governs what agents are permitted to do, and makes every autonomous decision traceable and accountable.

Job Description

QAD AI is building the Persona Agent platform — the product that lets manufacturers deploy AI agents across procurement, sourcing, order management and planning. As we scale deployments through our Forward Deployed Engineering motion, the platform has to mature just as fast as the field learns. That is this role.

The Senior Applied AI Product Manager owns the Persona Agent platform roadmap end-to-end. You decide what the platform does out-of-the-box, what becomes reusable, and what stays a one-off — turning the signal coming back from real customer deployments into a coherent, defensible product. You report directly to the Head of AI and are the product counterpart to the field.

What You'll Own

The Persona Agent platform roadmap

You own the roadmap for the platform and its agent capabilities — the vision, the priorities, and the trade-offs. You decide what gets built, in what order, and why, and you hold the line on it.

Vision & strategy: Set and maintain the platform roadmap against a clear thesis of where agentic manufacturing software is going.
Prioritisation: Own the backlog, prioritisation and sequencing across competing customer and internal demands.
Platform vs. custom: Decide what belongs in the platform vs. what stays a customer-specific build — the single most important call you make repeatedly.
Agent scope: Define what a Persona Agent should do out-of-the-box for each process (P2P, O2C, S2C, P2M) and where the boundaries sit.
The field-to-product loop

The FDE team and AI Solutions Architects are deploying agents inside real customers every week, and each engagement surfaces patterns, gaps and hard-won solutions. You are the person who catches that signal and decides what becomes product. This loop is the engine of the platform — without it, the roadmap is guesswork; with it, every deployment makes the product better.

Capture: Run a structured intake of field signal — product gaps, recurring exception logic, integration patterns, autonomy models — from the FDE team and Architects.
Triage: Decide which field solutions are one-offs and which are patterns worth productising into reusable platform capabilities.
Productise: Turn recurring field builds (e.g. EDI error handling, chat-driven PO creation, per-category autonomy dials) into first-class product features.
Communicate: Close the loop back to the field so Architects and FDEs know what's coming and can design against it.
Delivery with engineering

You work hand-in-hand with the AI engineering and Applied AI teams to ship the roadmap — writing crisp specs, making scope calls, and keeping delivery honest against outcomes rather than output.

Specs: Write clear product specs and acceptance criteria that engineers can build against without ambiguity.
Metrics: Define how each capability's success is measured — adoption, agent performance, deployment-time saved — and track it.
Trade-offs: Make the hard scope trade-offs in the moment, and own the consequences.
Evaluation & quality of agent behaviour

Agent products live or die on whether the agent behaves. You own the product view of agent quality — defining what “good” looks like, and making sure evaluation is built into the platform, not bolted on.

Evals: Define the evaluation criteria for agent behaviour per process and per capability.
Instrumentation: Ensure attribution and telemetry are product requirements, so every deployment can prove impact.
Trust: Set the guardrails and autonomy defaults that keep agents safe and trustworthy in production.

How You Work With The Rest Of The Org

You sit in the AI organisation reporting to the Head of AI, at the centre of the product, engineering and field triangle.

AI Solutions Architects - Primary source of field requirements and validation; you turn their designs into repeatable product.
FDE team (Services) - Your richest signal on what recurs across builds; you productise their accelerators.
Applied AI / Engineering - Your build partners; you spec, prioritise and ship the roadmap with them.
Head of AI - Your manager and the roadmap's executive owner; you bring the plan, the trade-offs and the field evidence.

Qualifications

What You'll Bring

Must-have

5–10 years in product management, with a track record owning a technical platform or developer-facing / enterprise product roadmap end-to-end.
Technical fluency with modern AI — you understand agents, LLMs, tool use, RAG, evals and autonomy well enough to make sound product calls and earn engineers' trust, without needing to build it yourself.
Demonstrated ability to turn messy, real-world signal into a coherent roadmap — and to say no to most of it.
Strong sense for the platform-vs-custom boundary: what to generalise, what to leave bespoke, and how to avoid a roadmap of one-offs.
Excellent written communication — crisp specs, clear prioritisation rationale,