Data Engineer (Python, AI/ML, RAG)

Role Overview

Design and develop intelligent AI-based applications using advanced NLP and LLM techniques to solve real-world business challenges in financial services. This role involves building and optimizing Retrieval-Augmented Generation (RAG) pipelines and working as an individual contributor to implement cutting-edge AI solutions.

Responsibilities

• Design and develop intelligent AI-based applications using advanced NLP and LLM techniques.
• 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) using frameworks like FastAPI or Flask.
• Implement semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases such as FAISS, Pinecone, or Weaviate.
• Perform NLP tasks including entity recognition, text classification, intent detection, and sentiment analysis.
• Monitor and fine-tune LLM/SLM performance and utilize LLMOps tools for production monitoring and evaluation.
• Build, train, and evaluate deep learning and traditional machine learning models.

Requirements

• Proven experience in building RAG pipelines and working with LLMs/SLMs.
• Strong understanding of advanced prompting techniques.
• Ability to work as an Individual Contributor with a positive, problem-solving attitude.
• Experience with vector databases and semantic search implementation.

Skills

• Python
• LLMs
• RAG
• FastAPI
• Vector Databases

Benefits

• Paid sick time
• Paid time off
• Provident Fund