Role Description

REQUIREMENTS: Strong expertise in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face. Solid understanding of supervised/unsupervised learning, data preprocessing, and feature engineering. Experience with model evaluation metrics (accuracy, precision, recall, F1-score). Familiarity with banking AI use cases such as fraud detection, personalization, credit scoring, and churn prediction. Hands-on experience with cloud ML platforms (Azure ML, AWS SageMaker, Google Vertex AI). Knowledge of MLOps tools like MLflow and Kubeflow for CI/CD, model lifecycle management, and monitoring. Ability to develop explainable AI models using SHAP, LIME, and ensure regulatory compliance. Strong skills in NLP and LLM engineering, including fine-tuning, prompt engineering, and RAG-based architectures. Knowledge of vector databases (FAISS, Pinecone), orchestration tools (LangChain, LlamaIndex), and conversational AI frameworks. Strong backend integration capabilities using REST APIs, Docker/Kubernetes, and microservices. Preferred certifications: TensorFlow Developer, AWS ML Specialty, Google Professional ML Engineer. RESPONSIBILITIES: Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements. Mapping decisions with requirements and be able to translate the same to developers. Identifying different solutions and being able to narrow down the best option that meets the client’s requirements. Defining guidelines and benchmarks for NFR considerations during project implementation Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed. Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it Understanding and relating technology integration scenarios and applying these learnings in projects Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken. Carrying out POCs to make sure that suggested design/technologies meet the requirements.

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.