Lead AI Product Engineer
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Before you apply
Location2 Locations
Typefull-time
SeniorityLead
Experience10–14 yrs
Company size1,001–5,000 people
PostedSep 22, 2026 · 1mo ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
Ten to fourteen years in AI, machine learning or software
At least three years leading a team
Track record of owning technical design, not only implementing someone else's design
Production experience with LLM and agentic systems: multi-step workflows, tool use, orchestration frameworks, and the failure modes that only appear at scale
Real evaluation discipline. Evidence that you have measured agent quality with something more rigorous than manual spot checks
Strong Python engineering
Document understanding and information extraction experience, ideally on messy real-world inputs such as emails, remittance advices and attachments
Classical machine learning depth alongside the generative work, and the judgement to know which problem needs which
Enough understanding of services, data and deployment to build agents that fit the platform and to hold your side of a design argument with the Engineering Lead
Product track record: AI built for many clients, not one-off delivery for a single engagement
Work in a company where people truly BELIEVE in what they are doing
Order-to-cash or accounts receivable domain knowledge: collections, cash application, deductions, remittance handling
Experience with agent observability and tracing tooling
Experience getting AI through enterprise risk, compliance or model governance review
Skills
Next.js
FastAPI
PostgreSQL
Azure Databricks
Python
About the company
It’s fun to work in a company where people truly BELIEVE in what they are doing!
We’re committed to bringing passion and customer focus to the business.
The role
Cogentiq I2C is Fractal’s agentic AI Product for invoice-to-cash, covering Collections, Cash Application, Deductions, Invoice Management and Credit Risk. It runs on a Next.js front end, a FastAPI service layer, the Cogentiq agentic runtime, and a data tier on Azure Databricks with PostgreSQL.
You own the AI side: the agents, the models and everything that makes them accurate and dependable in front of a finance team. You design it, you lead the team that builds it, and you write code yourself.
What you will own
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Technical design of the AI side: agent decomposition, orchestration patterns, tool design, memory and context strategy.
•
The agents across all five modules, from document extraction and matching through to the reasoning steps behind a recommendation.
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Accuracy and reliability. Evaluation sets, regression suites, and a defensible number for how well each agent performs before it ships.
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Guardrails and human oversight: confidence thresholds, escalation to a person, audit trails, and traceable reasoning for any decision touching cash.
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Predictive and machine learning models where they serve the product better than an agent does.
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Model strategy: choice of models, cost and latency per workflow, and the ability to switch providers without a rewrite.
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The interfaces between your side and the platform, agreed jointly with the engineering.
How you will work
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Lead the AI and agent engineering team. Set standards, coach engineers, and keep the team unblocked.
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Stay in the code. Take the hardest agents yourself and set the patterns others follow.
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Work as a pair with the Engineering Lead.
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Work with product management on what an agent can realistically be trusted to do, so external commitments are grounded.
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Be able to explain an agent’s behaviour to a finance stakeholder who will not accept “the model decided”.
What you must have
•
Ten to fourteen years in AI, machine learning or software, with at least three leading a team.
•
Track record of owning technical design, not only implementing someone else’s design.
•
Production experience with LLM and agentic systems: multi-step workflows, tool use, orchestration frameworks, and the failure modes that only appear at scale.
•
Real evaluation discipline. Evidence that you have measured agent quality with something more rigorous than manual spot checks.
•
Strong Python engineering. Your team ships production code, not notebooks.
•
Document understanding and information extraction experience, ideally on messy real-world inputs such as emails, remittance advices and attachments.
•
Classical machine learning depth alongside the generative work, and the judgement to know which problem needs which.
•
Enough understanding of services, data and deployment to build agents that fit the platform and to hold your side of a design argument with the Engineering Lead.
•
Product track record: AI built for many clients, not one-off delivery for a single engagement.
Good to have
•
Order-to-cash or accounts receivable domain knowledge: collections, cash application, deductions, remittance handling.
•
Experience with agent observability and tracing tooling.
•
Experience getting AI through enterprise risk, compliance or model governance review.
If you like wild growth and working with happy, enthusiastic over-achievers, you’ll enjoy your career with us!
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