Lead AI Product Engineer

Fractal Analytics Inc.; Mumbai; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Fractal Analytics Inc. in Mumbai is seeking a Lead AI Product Engineer for Cogentiq I2C, Fractal’s AI product suite for invoice-to-cash. You will own the AI side: agents, models, and the end-to-end design that powers finance teams.

You will lead the AI/ML engineering team, set standards, and write production code yourself, collaborating with product management to ensure trusted, scalable agent solutions.

Qualifications

• 10–14 years in AI, ML or software with at least 3 years leading a team.
• Production experience with LLMs and agentic systems: multi-step workflows, tool use, orchestration frameworks.

Responsibilities

• Lead the AI and agent engineering team; set standards and coach engineers.
• Engage with product management to align capabilities with business goals.
• Own technical design of AI components, ensuring accuracy and defensible performance.

Skills

AI leadership

Python engineering

LLM & agentic systems

Document understanding

Job description

## Lead AI Product EngineerApply: Bengaluru: Mumbai: Full time: Posted Today: SR-45878It'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 roleCogentiq 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!