MLOps Engineer

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).