About VARTIS Platforms (Formerly known as LenDenClub Techserve Pvt. Ltd.)
At VARTIS Platforms, we are a corporate group on a mission to democratize credit access and empower millions of people through cutting-edge technology. We focus on building trust and convenience, leveraging AI to drive the information revolution for smarter risk management and operational efficiency. VARTIS Platforms is profitable, rapidly scaling, and industry-recognised. Our impact has been acknowledged with top honours, including being awarded as the Best Fintech Provider by Financial Express, a testament to our product excellence, customer experience, and leadership in regulated financial technology.
Our portfolio includes:
LenDenClub: An RBI-licensed P2P marketplace that connects borrowers with lenders in a seamless digital ecosystem.
InstaMoney: Instant digital loans that enable quick, secure, and accessible financial support.
NiveshGold: India’s next-gen digital vault for wealth preservation, starting with 100% pure, insured 24K digital gold.
UdharSaathi: A structured working capital platform built exclusively for QR active kirana stores, pharmacies, and small retailers.
Role Summary
You will build the AI that stops fraud at LenDenClub before money moves: graph models that expose fraud rings, and face match and liveness models that confirm a real, present person is behind every application. You own these models from data to production.
- Fraud rings: model borrowers, devices, phones, bank accounts, UPI handles, addresses and IPs as one graph, and catch coordinated networks that single-application rules miss.
- Identity: verify that the selfie matches the KYC photo and that the face is live, not a print, replay, mask, deepfake or injected camera feed.
- Real stakes: your models sit in the live onboarding and disbursal flow of an RBI-regulated lender, where a miss means a loss and a false alarm means a lost genuine customer.
Key Responsibilities
- Design the entity graph: nodes, edges and features across applications, devices, bank accounts, UPI handles, contacts and addresses.
- Build GNN models (GraphSAGE, GAT, heterogeneous and temporal GNNs) for node classification, link prediction and fraud-ring detection.
- Combine GNNs with community detection (Louvain, Leiden), graph features and tabular models where they perform better.
- Build and tune face match on KYC photos (Aadhaar, PAN, DigiLocker, Video KYC captures) using ArcFace-class embeddings.
- Build passive and active liveness and presentation-attack detection against prints, replays, masks, deepfakes and virtual-camera injection.
- Measure performance by device tier, lighting and skin tone, and close gaps before they reach customers.
- Deploy low-latency inference for real-time onboarding, including on-device models (ONNX, TFLite) where needed.
- Work with risk, operations and investigation teams to label cases, explain alerts and turn confirmed fraud into training data.
Mandatory Skills
- 3+ years in machine learning with models shipped to production in fraud, risk, identity or a comparable adversarial domain.
- Hands-on GNN work with PyTorch Geometric or DGL on real, large, noisy graphs, not only benchmark datasets.
- Hands-on computer vision for face recognition or face anti-spoofing, with evaluation using FAR/FRR or APCER/BPCER.
- Strong Python, PyTorch and SQL; able to build features from raw event and identity data yourself.
- Clear written and verbal communication: you can explain why a cluster was flagged to an investigator and a risk head.
Preferred Skills
- Graph databases such as Neo4j or TigerGraph, and streaming graph updates.
- Fraud domains: mule accounts, synthetic identities, loan stacking, collusive borrower networks.
- Deepfake and injection-attack detection; familiarity with ISO/IEC 30107-3 presentation attack testing.
- Indian KYC flows: Aadhaar e-KYC, CKYC, DigiLocker and Video KYC.
- Learning with scarce, delayed labels: semi-supervised, anomaly detection, active learning.
- Model explainability for graph and vision models.
- Handling biometric data under the DPDP Act.
This Role Is Not For You If
- Your fraud experience is limited to rules engines or threshold tuning.
- You have only integrated third-party face match or liveness APIs, never trained or evaluated such models.
- You have worked with graphs only on academic datasets.
What You Get
- Ownership of fraud and identity AI for a platform with over 2 Cr users.
- Real fraud labels and investigator feedback to learn from, not synthetic benchmarks.
- Direct work with the Chief of AI, Risk and Operations leadership.
- A path to lead the fraud AI function as it grows.
