Technical Product Manager , AI Solutions

Technical Product Manager , AI Solutions
Location: Remote with client travel up to 50% (Paid by client)
Practice: AI & Data Solutions
About the Role
We are looking for an experienced Technical Product Manager to lead small,
multidisciplinary teams delivering complex AI and data solutions using platforms such
as Uniphore, Palantir and AWS AI services.
This is not a traditional Project Manager, Scrum Master or Product Owner role.
Customers often bring us business outcomes ("I need to understand where we are
losing margin") rather than detailed requirements, and business rules, data quality and
success measures are often undefined. Your job is to create clarity from that ambiguity
and lead the team to a measurable outcome.
You will combine the skills of a Business Analyst, Product Owner and Agile Project
Lead, leading engagements from discovery through deployment and business
acceptance.
What This Role Isn't
This isn't the right role if you mainly want to facilitate Agile ceremonies, manage
somebody else's plan, hand requirements over to engineering, or focus on schedule
and status reporting. We want someone who owns the problem-solving and delivery
process end to end.
What You'll Do
Turn Business Ambition into Measurable Outcomes
Work directly with C-suite stakeholders and Private Equity operating teams to translate
strategic goals into measurable KPIs and success criteria, clarify what "correct" means
and who approves it, define what must be proven for acceptance, and keep engineering
work tied to executive objectives.
Lead Discovery and Requirements Definition
Own requirements discovery through executive sessions, workshops, and process, root-
cause, business-rule and data analysis, producing outcome statements, process maps,
user stories, rule catalogs, acceptance criteria and traceability. Critically, you'll know
when the team does not yet understand a requirement well enough to engineer it.
Own the Product and Delivery Backlog
Define MVP boundaries, prioritize the backlog, break outcomes into testable increments
and facilitate scope decisions, discussing business value with executives in the morning
and acceptance criteria with engineers in the afternoon.
Lead a Small, High-Performing Technical Team
Lead Forward Deployed, AI, data and platform engineers, architects, data scientists, QA
specialists and customer SMEs. You don't need to be the strongest engineer in the
room, but you need enough technical fluency to challenge assumptions, understand
dependencies and identify risk.
Bring a Data-First Mindset to AI Delivery
Investigate data readiness early (sources, quality, lineage, ownership and reconciliation)
and recognize when an apparent AI problem is really a business-rule, process or data-
quality problem that must be solved first.
Make Quality Part of the Engineering Process
Build quality in from discovery, not just before UAT, coordinating business-rule, data,
functional, integration, AI evaluation and user acceptance testing with traceability from
business outcome to acceptance. For AI, define how accuracy, grounding, consistency,
explainability and human oversight are assessed.
Lead Delivery with Pragmatism
Apply Scrum, Kanban or hybrid approaches pragmatically, owning planning, RAID,
scope and change control, executive reporting, governance and escalations, with a
focus on reducing uncertainty and demonstrating value quickly.