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
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Own the full ML lifecycle: data prep, feature pipelines, training, deployment, monitoring and automated retraining.
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Build and maintain CI/CD pipelines for ML with containerized deployments (Docker, Kubernetes).
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Design scalable data/ETL workflows (batch & streaming) using PySpark and a workflow orchestrator (e.g., Airflow).
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Set up a feature store and model registry for reproducible, governed model delivery.
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Implement model monitoring and drift detection (data drift, concept drift, performance decay) and trigger retraining.
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Build observability for model and API health — metrics, logs, alerts, dashboards.
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Optimize cloud/infrastructure costs (autoscaling, spot instances, right-sizing) and track cost per model.
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Enforce ML governance — versioning, lineage, audit logs, and compliance requirements.
Nice to Have
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Experience with streaming data (Kafka / Flink).
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Familiarity with feature stores (e.g., Feast).
