Lead AI Product Engineer

Fractal; Bengaluru; On-site; INR 12,000,000 - 19,000,000; Full time

Job summary

Fractal is seeking an experienced AI leader to own the agentic AI stack for Cogentiq I2C, Fractal's invoice-to-cash product. You will direct the design of AI agents, manage a capable engineering team, and write significant code.

You will drive model choices, evaluation discipline, and governance across complex, multi-module workflows that support finance teams. You will lead development across document extraction, reasoning, and decision-making, ensuring scalable, observable and auditable AI.

Qualifications

• Ten to fourteen years in AI, ML or software, with at least three leading a team.
• Production experience with LLM and agentic systems: multi-step workflows, tool use, orchestration.
• Strong Python engineering; production code over notebooks.
• Experience with document understanding and information extraction on real-world inputs.
• Judgement to choose appropriate model types and system designs.

Responsibilities

• Lead the AI and agent engineering team; set standards and coach engineers.
• Own technical design of AI side: agent decomposition and memory/ context strategy.
• Drive accuracy, reliability, evaluation suites, and defensible performance metrics.
• Define guardrails: confidence thresholds, escalation, audit trails, and traceable reasoning for cash-related decisions.
• Collaborate with Engineering Lead on interfaces between AI components and the platform.
• Explain agent behaviour clearly to finance stakeholders.

Skills

AI leadership

Production ML

Python engineering

LLM & agentic systems

Model governance

Document understanding

Systems design

Job description

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

• 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.
• Accuracy and reliability. Evaluation sets, regression suites, and a defensible number for how well each agent performs before it ships.
• Guardrails and human oversight: confidence thresholds, escalation to a person, audit trails, and traceable reasoning for any decision touching cash.
• Predictive and machine learning models where they serve the product better than an agent does.
• Model strategy: choice of models, cost and latency per workflow, and the ability to switch providers without a rewrite.
• The interfaces between your side and the platform, agreed jointly with the engineering.

How you will work

• Lead the AI and agent engineering team. Set standards, coach engineers, and keep the team unblocked.
• Stay in the code. Take the hardest agents yourself and set the patterns others follow.
• Work as a pair with the Engineering Lead.
• Work with product management on what an agent can realistically be trusted to do, so external commitments are grounded.
• 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!