About Us:
Paytmis India's leading digital payments and financial services company, which is focused on driving consumers and merchants to its platform by offering them a variety of payment use cases. Paytm provides consumers with services like utility payments and money transfers, while empowering them to pay via Paytm Payment Instruments (PPI) like Paytm Wallet, Paytm UPI, Paytm Payments Bank Netbanking, Paytm FASTag and Paytm Postpaid - Buy Now, Pay Later. To merchants, Paytm offers acquiring devices like Soundbox, EDC, QR and Payment Gateway where payment aggregation is done through PPI and also other banks' financial instruments. To further enhance merchants' business, Paytm offers merchants commerce services through advertising and Paytm Mini app store. Operating on this platform leverage, the company then offers credit services such as merchant loans, personal loans and BNPL, sourced by its financial partners.
Position Summary:
We are looking for a Senior Machine Learning Engineer, to work on developing large-scale big-data machine learning & solution automation toolkits and libraries. In this role, you will work with a talented engineering and data science team to develop a state-of-the-art ML framework that will enable hundreds of solutions and users, while also having the opportunity to research the latest machine learning techniques in industry and academia and then bring them into the Paytm Labs data scientist community.
What does this include?
Collaborate closely with data scientists to transform machine learning models from prototypes into production-ready solutions; work with data engineers to create robust data pipelines, ensuring data quality and appropriate feature engineering for model inputs
Develop and deploy monitoring solutions to track model performance, detect anomalies, and maintain model health; implement metrics to monitor and measure model and strategy impact
Implement strategies for retraining models in response to data drift and model performance degradation
Help to establish standards, approaches and implement required tooling and automation to support the full life cycle for model design and development which includes, but is not limited to identifying objectives, sampling, testing/validation, calibration, and monitoring performance
Contribute to the development of predictive modeling projects using data mining techniques for estimating current and future member engagement, operational performance, or other business outcomes; apply unsupervised learning methods to augment existing supervised models, or detect portfolio anomalies
Requirements:
4-7 years' work experience as a Machine Learning Engineer or similar role.
Excellent understanding of machine learning frameworks (Keras/Tensorflow/PyTorch etc.) and libraries (scikit-learn, etc.)
In depth understanding and hands-on in MLOps Infrastructure and tooling, such as Containerization, ML Workflow specification as DAG and management with Scheduling/Orchestration frameworks, ML platform for feature and model store
Excellent understanding of computer science fundamentals, data structures, and algorithms.
Familiar with object-oriented design methodology and application development in Python.
Familiar with big data-related technologies to manage large volumes of complex data (SQL, pyspark).
Think outside of the box. Willing to have both hard work and have fun.
Ability to work in a team and highly collaborative
Working experience in fraud risk is a plus
Knowledge of Scala language is a plus
BS, MS, or PhD in Computer Science or related technical discipline (or equivalent).