Role Overview

The role requires hands-on experience in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, and modern AI orchestration frameworks. A strong foundation in Machine Learning and advanced analytics is also essential.

Experience in Corporate or Institutional Banking is preferred, especially in domains such as credit, trade finance, treasury, compliance, or relationship management analytics.

Key Responsibilities:
- Design and develop Generative AI solutions using LLMs and RAG frameworks.
- Work on prompt engineering, embeddings, and vector database integration.
- Develop and deploy Machine Learning models for business use cases.
- Collaborate with business, engineering, and data teams to deliver scalable AI solutions.
- Optimize model performance, accuracy, and response quality.
- Deploy and monitor AI/ML models in production environments.
- Follow AI governance, security, and best practices.
- Stay updated with the latest trends in Generative AI and Machine Learning.

Required Skill Set




- 4+years of experience in machine learning, deep learning, or Al research, With a focus on generative models.
- Solid experience in Generative AI and Large Language Models (LLMs).
- Autoencoders), and transformer-based models (e.g., GPT-3/4, BERT, DALL.E).
- Understanding of model fine-tuning, transfer learning, and prompt engineering in the context Of large language models (LLMS).
- Knowledge of reinforcement learning (RL) and other advanced machine learning techniques applied to generative tasks.
- Hands-on experience with RAG, prompt engineering, and embeddings.
- Experience with LangChain, LlamaIndex, or similar frameworks.
- Strong understanding of Machine Learning concepts and model development.
- Proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
- Experience with APIs, model integration, and deployment workflows.
- Knowledge of vector databases and AI solution architectu

📌 Manager (Bengaluru)
🏢 Course5
📍 Bengaluru