Roles & Responsibilities
- Design and develop intelligent AI-based applications using advanced NLP and LLM techniques to solve real-world business challenges in financial services.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging structured and unstructured financial data.
- Integrate and orchestrate LLMs/SLMs for question-answering, summarization, semantic search, and document understanding.
- Develop and maintain RESTful APIs (sync and async) to serve NLP models and chatbot interfaces using frameworks like FastAPI, Flask, etc.
- Should have knowledge of advanced prompting techniques.
- Implement semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases (e.g., FAISS, Pinecone, Weaviate).
- Perform NLP tasks such as entity recognition, text classification, intent detection, embedding generation, and sentiment analysis where required.
- Monitor and fine-tune LLM/SLM performance with real-world user data to improve relevance, latency, and accuracy.
- Exposure to LLMOps tools for monitoring, evaluation, and versioning of AI models in production.
- Build, train, and evaluate deep learning models for NLP tasks including classification, NER, summarization, and embedding generation.
- Develop traditional machine learning models (e.g., regression, decision trees, clustering) for structured data analysis and prediction tasks.
- Interact with cross-functional teams to understand system issues and follow up with respective teams to get them fixed.
- Understand and identify areas of improvement across businesses and participate in solution identification and implementation.
- Should be able to work as an Individual Contributor on new and existing projects.
- Positive and problem-solving attitude, must work as an independent contributor.
Skills: ai/ml,nlp,data,models,learning,vertex,semantic search,gcp