About LenDenClub:
LenDenClub is India’s largest RBI-registered (NBFC-P2P) Peer-to-Peer (P2P) lending platform. We are a platform for lenders seeking high interest with creditworthy borrowers, bridging the gap left by traditional credit institutions. With over 3 crore users and ₹16,000 crore+ in loans disbursed, we command more than 98% of India’s P2P lending market. Our 4.4+ rating on the App Store reflects our commitment to offering a trustworthy and secure lending experience. Powered by cutting-edge technology and a user-first approach, we are setting new benchmarks in India’s evolving fintech ecosystem. The progressive approach towards employee benefits has been acknowledged and appreciated and as a result, LenDenClub has been certified as a 'Great Place to Work' successively for four years by the Great Place to Work Institute, Inc.
About InstaMoney:
InstaMoney is our cutting-edge Loan Service Provider (LSP) platform, built to make borrowing fast, flexible, and fully digital for users across India. With over 30 million downloads, InstaMoney offers seamless credit access through a simple and intuitive mobile app. From Personal Loans and Merchant Loans, InstaMoney provides short- to mid-tenure credit solutions.
Profile Summary:
We are looking for a hands-on MLOps Engineer to own the end-to-end ML lifecycle — from data pipelines to production deployment, monitoring and retraining — with a focus on reliability, automation and cost efficiency.
Key Responsibilities
Own the full ML lifecycle: data prep, feature pipelines, training, deployment, monitoring and automated retraining.
Build and maintain CI/CD pipelines for ML with containerized deployments (Docker, Kubernetes).
Design scalable data/ETL workflows (batch & streaming) using PySpark and a workflow orchestrator (e.g., Airflow).
Set up a feature store and model registry for reproducible, governed model delivery.
Implement model monitoring and drift detection (data drift, concept drift, performance decay) and trigger retraining.
Build observability for model and API health — metrics, logs, alerts, dashboards.
Optimize cloud/infrastructure costs (autoscaling, spot instances, right-sizing) and track cost per model.
Enforce ML governance — versioning, lineage, audit logs, and compliance requirements.
Nice to Have
Experience with streaming data (Kafka / Flink).
Familiarity with feature stores (e.g., Feast).
