- 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.

Requirements :

- Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

- Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.

- Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.

- Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

- Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models.

- Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

- Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

- Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.