Description : We are seeking an experienced Machine Learning Engineer with strong expertise in Generative AI (GenAI) and payment data analytics to join our team. In this role, you'll work on large-scale credit card and UPI transaction data to identify use cases, develop PoCs, and build production-ready ML and GenAI solutions that unlock insights and drive innovation in payments.

Key Responsibilities :

Data Handling & Feature Engineering :

- Collect, preprocess, and analyze payment datasets (credit card, UPI) to extract meaningful features, including embeddings, time-series signals, and customer behavior patterns.

ML Model Development :

- Design and implement models for fraud detection, customer segmentation, and demand forecasting using tools like TensorFlow, PyTorch, and Scikit-learn.

- Generative AI & Foundation Models :

Apply and fine-tune OSS LLMs (e.g., Hugging Face Transformers, Llama, GPT-J, Falcon) for use cases such as :

- Synthetic data generation

- Transaction summarization

- Conversational AI for payment-related queries

- Model Evaluation & Optimization :

Use metrics like AUC-ROC, precision, recall, and F1-score to evaluate models. Apply hyperparameter tuning and distributed training for performance improvement.

- Deployment & MLOps :

Deploy ML/GenAI models using tools like MLflow, Kubeflow, SageMaker, and Docker/Kubernetes. Set up real-time inference pipelines and monitor model drift and reliability in production.

- Collaboration & Communication :

Work with cross-functional teams (data scientists, engineers, product) to define requirements and deliver business-impacting solutions. Present findings to both technical and non-technical stakeholders.

Requirements :

- Bachelors or masters degree in computer science, engineering, mathematics, statistics, or a related field.

- 8 - 10 years of proven experience as a Machine Learning Engineer, Data Scientist, or similar role.

- Strong knowledge of machine learning, statistics, and data science concepts and techniques.

- Proficient in programming languages such as Python, R, and Java.

- Experience with data processing tools like SQL, Spark, and Hadoop.

- Proficient in using frameworks like TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers, and other OSS LLMs.

- Hands-on experience with foundational models, such as BERT, GPT, T5, and Vision Transformers (ViT).

- Experience with credit card and UPI payment data use cases (e.g., fraud detection, transaction risk assessment, customer analytics) is a plus.

- Experience fine-tuning and deploying OSS LLMs for specific tasks such as text summarization, synthetic data generation, and NLP applications in payments.

- Familiarity with frameworks/tools like Hugging Face, LangChain, and LlamaIndex.

- Hands-on experience deploying ML and GenAI models in production environments with tools like MLflow, Kubeflow, Docker, and Kubernetes.

- Excellent communication and presentation skills, with the ability to explain complex concepts