Data Scientist/AI Engineer - Generative AI

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.

Ideal Candidate:1. Profile:

• Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2. Mandatory Experience:

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

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

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

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