Machine Learning Engineer

About Indian Digital Payment Intelligence Corporation (IDPIC)
Indian Digital Payment Intelligence Corporation (IDPIC) is a Section 8 company, incorporated under the Companies Act, 2013. It is promoted by the State Bank of India (SBI) and Bank of Baroda, along with other banks/payment companies, and functions under the regulatory approval and oversight of the Department of Financial Services and the Reserve Bank of India (RBI). IDPIC has been established as the nation’s central digital payment fraud intelligence platform. Its primary mandate is to detect, prevent, and analyse fraud in India’s rapidly expanding digital payments ecosystem in real time. Leveraging cutting-edge technologies, including Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analytics, IDPIC plays a pivotal role in strengthening the security and trustworthiness of India’s digital payment infrastructure.
Position Overview:
The Machine Learning Engineer will be responsible for developing, training, deploying, and monitoring AI/ML models powering DPIP’s real-time transaction risk scoring, mule-account identification, and fraud-pattern detection. The role will continuously enhance model accuracy, performance, and resilience to address evolving fraud typologies and emerging payment risks.
Qualification and Eligibility Criteria:
Bachelor's degree in Computer Science, Data Science, Statistics, Artificial Intelligence / Machine Learning or a related field from a reputed institute
Preferred: MTech / MS / PhD in Machine Learning, Data Science or AI; relevant ML / cloud certifications (IIT Preferred)
Work Experience :
Minimum 1 year building and deploying production machine-learning models.
Strong in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost), feature engineering, anomaly / fraud detection, graph analytics, NLP and real-time inference and other related frameworks. Experience with big-data tooling (Spark, Flink, Kafka) and MLOps (MLflow / Kubeflow, model monitoring); fraud-analytics or payments domain preferred. Comptence in Model Risk Management, Responsible AI frameworks.
Key Responsibilities:
ML Model Development: Develop, train, deploy, and maintain AI/ML models for real-time fraud detection, transaction risk scoring, anomaly detection, and mule-account identification.
Model Monitoring & Optimisation: Continuously monitor model performance, accuracy, and effectiveness, and enhance models to address evolving fraud patterns and typologies.
Data Science & Analytics: Conduct data analysis and develop reports, dashboards, and actionable insights to support fraud risk management and institutional decision-making.
Data Governance & Collaboration: Ensure data quality, validation, documentation, and governance while collaborating with technology, product, operations, compliance, and business teams.
Innovation & Advanced Analytics: Develop and pilot AI/GenAI-enabled solutions, analytical tools, and intelligence use cases to strengthen fraud intelligence and payment security.

Work arrangement
No