Position Overview :

Key Responsibilities :

- Design, develop, and deploy conversational AI systems (chatbots, virtual assistants) that leverage LLMs to enable seamless voice interactions for banking services & Code generation and Completion including test cases.

- Develop multi modal solution architecture with Agents with bespoke models for specific use-case/scenario

- Fine-tune and optimize LLM models for specific banking tasks and user experiences.

- Implement Retrieval Augmented Generation (RAG), and embeddings to retrieve customer information through APIs

- Continuously monitor and evaluate the performance of voice-based banking solutions.

- Develop and deploy machine learning models for tasks like personalized recommendations, and customer sentiment analysis.

- Collaborate to deploy and manage models on GCP, leveraging its MLOps tools and services to streamline model development, training, and deployment.

- Develop an end-to-end architecture from training to inferencing custom models

Skills :

- Strong understanding of natural language processing (NLP) concepts and techniques, good programming skills

- Knowledge on AI/ML algorithms building NLP Applications

- Deep knowledge of LLM architectures and their applications.

- Expertise in data preprocessing, feature engineering, and model evaluation.

- Basic understanding of MLOps principles and practices.

- Excellent problem-solving and communication skills.

Experience :

- 3+ years of hands-on experience in AI/ML development, with a strong focus on NLP & API Integrations

- Experience with LLM models, and fine-tuning, prompt engineering, and RAG techniques.

- Proficiency in Python is Must

- Knowledge on vector databases will be good

- Hands-On experience with frameworks TensorFlow, Pytorch, LangChain, FastAPIs, NoSQL Database

- Knowledge on GCP/MLOPs will be a plus