We are hiring an AI Product Manager to embed directly inside one of our core AI initiatives. In this hands-on role, you will lead discovery and design, work shoulder-to-shoulder with business teams, and manage execution alongside specialist engineering partners. We need someone who thrives in ambiguous environments, owns their results end-to-end, and can take a messy problem and turn it into a clear design. On day one, you will sit with functional experts, map out their daily workflows, and redesign them from scratch using modern AI tools.
Reporting to the Chief Credit Officer, you will operate with high day-to-day autonomy inside your pod. This position is ideal for an early-career product manager looking to build deep AI expertise fast, take real ownership of production outcomes, and grow alongside our expanding team.
Current State Discovery & Process Intelligence- Deep-Dive SME Discovery: Lead structured discovery sessions with functional Subject Matter Experts (SMEs) to map the current process in full - every step, decision point, pain point, exception case, volume estimate, and cycle time. The goal is to understand how the process actually works, not how it is documented.
- Metric Baselining: Quantify the current state - cycle times, error rates, manual touchpoints, cost of failure - to create the measurement foundation against which post-deployment impact will be assessed.
- Edge Case Extraction: Surface the process edge cases, informal workarounds, and undocumented exceptions that SMEs carry in their heads but rarely write down. These are often where AI creates the most value - and where designs fail if not addressed.
- Hypothesis Testing: Work with the AI Product Director to test design hypotheses during discovery - some will be confirmed by the process reality, some will need to be revised. Discovery should challenge assumptions, not confirm them.
AI Solution Design & Brief Authorship- 0-to-1 Reimagining: Translate current state intelligence into an AI transformation design brief that reimagines the process from scratch - not an AI-assisted version of the status quo. The question to answer is: if we were designing this process from scratch with AI available, what would it look like? Vision and ability to think outside of the box is mandatory
- Design Brief Production: In close collaboration with the AI Product Director, author a detailed solution design brief that defines the AI capability, its inputs and outputs, the logic it applies, the QC framework it requires, and the acceptance criteria the engineering partner must meet. This brief is the specification the partner builds to - precision matters.
- Partner Identification: Conduct research to identify specialist engineering partners with a demonstrated track record in the specific use case domain. Apply Mulligan's IP sensitivity framework to determine the appropriate engagement model.
- Go/No-Go Recommendation: Produce a clear go/no-go recommendation to the AI Product Director with supporting rationale - including current state baseline, proposed AI solution design, recommended partner, and initial cost-benefit framing.
Engineering Partner Management- Day-to-Day Partner Interface: Manage the working relationship with the specialist engineering partner through the build cycle - coordinating build reviews, tracking progress against the design brief, and keeping delivery on schedule.
- Deliverable Review: Review partner outputs against the design brief and Mulligan's acceptance criteria - identifying divergences early and escalating material gaps to the AI Product Director before they become rework cycles.
- Iterative Build Cycle: Facilitate the review-feedback-refine loop between the engineering partner and Mulligan's internal QC and functional stakeholders - ensuring the build converges on a production-grade output, not a minimum viable approximation.
- Vendor Discipline: Enforce Mulligan's core partnership principle: Mulligan authors the specification; the partner builds to it. Never allow the partner to define the scope or shape the design.
QC Framework & Evaluation Support- Pre-Build QC Design: Design the QC framework for the pod's use case during Phase 1 - before the engineering build begins. Define the human review process, the deterministic checks the DS person will build, and the LLM-as-judge rubric that will evaluate output quality.
- Baseline Evaluation: Collaborate with the pod's DS person to establish evaluation rubrics, golden datasets, and accuracy thresholds that define what 'production ready' means for this specific use case.
- First Impression Discipline: Ensure the first outputs from any AI agent are seen only by the QC function before being shown to end users. The first impression determines the adoption trajectory - a rough tool shown prematurely is very hard to recover from.
Go-Live & End-User Adoption- Shadow Run Coordination: Coordinate the shadow run period - the side-by-side comparison between AI output and the current process that converts end-user sceptics into advocates. This is non-negotiable and must be time-bounded.
- Adoption Planning: Work with the functional head to build the end-user adoption plan - ensuring the user community feels they helped shape the tool, not that it was imposed on them.
- Post-Launch Tracking: Track post-deployment metrics against the pre-implementation baseline to document the value created and surface the feedback the DS person needs to refine the model.
Required Experience & Education- 3-5 years of experience in product management, product analysis, or a closely related role - with direct exposure to AI-enabled products, not just software delivery.
- Demonstrated ability to lead structured discovery with domain experts - extracting precise process intelligence, surfacing undocumented edge cases, and translating those findings into a clear design brief without being given the answer.
- Prior experience at an AI-native fintech, lending company, or high-velocity product environment is strongly preferred. This role requires the instinct to reimagine a process with AI rather than automate it. Candidates who have only worked in waterfall delivery environments or large enterprise software teams are unlikely to have the transformation energy this role demands.
- Experience authoring clear written product artefacts - process maps, design briefs, requirements documents, or specifications - that others have built to.
- Comfortable managing ambiguity and structuring undefined problems without waiting for direction. The ability to own a phase of work end-to-end and produce a clean output at the end of it.
- Experience coordinating with external engineering vendors or technology partners is a plus but not required. A willingness to learn partner management and hold partners to a specification is essential.
- Bachelor's Degree in Business, Computer Science, Engineering, Economics, or a related field is required.
- Master's degree is a plus but evaluated secondary to demonstrated product delivery experience and a track record of working on AI-enabled products.
Functional & Technical Skills- AI Product Fluency: Working familiarity with how LLM-enabled products and AI agents are designed and deployed - sufficient to have a substantive conversation with an engineering partner about what is being built and why, and to identify when a proposed solution does not match the design intent. Credit or lending domain expertise is not required.
- Process Discovery & Intelligence Extraction: The ability to draw out precise process knowledge from functional SMEs who have never mapped their workflow before - asking the right questions, surfacing the exceptions, and documenting what you hear with enough precision that it can be turned into a design brief.
- Design Brief Authorship: The ability to translate discovery findings into a clear, buildable specification - defining what the AI should do, what data it receives, what output it must produce, and what 'done' means. A document someone else can build to without coming back for clarification.
- Structured Problem Solving: The ability to take an ambiguous problem - an undefined process, a blank-page use case - and structure it into phases, questions, and outputs without being told what the answer should be. The transformation instinct: asking 'what could this look like with AI?' rather than 'how do we make what exists faster?'
- Stakeholder Trust-Building: The ability to earn the confidence of functional SMEs who may be protective of their current process - creating an environment where they share the full picture honestly, including the parts that are messy or inefficient. Adoption is earned during discovery, not after launch.
$170,000 - $250,000 a year
A reasonable estimate of the base compensation range for this role is $170,000 to $250,000 per year. Total compensation may include base salary and other forms of compensation, such as an annual bonus as applicable. In determining compensation, Mulligan Funding considers a variety of factors, including but not limited to market data, relevant experience, skills, education, and certifications.
