Data Science | Gen-AI | Natural Language Processing (NLP) | Python | SQL
• 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 • Must have 3+ years of hands-on experience in AI Engineering, Data Science, Machine Learning, Deep Learning, NLP, or Generative AI application development.
• Must have strong Python programming experience with backend development, REST API development, and production-grade application support.
• Must have hands-on experience with Deep Learning frameworks such as PyTorch, TensorFlow, or Keras for building NLP and classification models.
• Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, or similar models.
• Must have hands-on experience in building RAG (Retrieval-Augmented Generation) applications, semantic search solutions, and vector-based retrieval systems.
• Must have practical experience with Prompt Engineering, LLM fine-tuning, embeddings, and AI-powered business applications.
• Must have hands-on experience with LangChain, LangGraph, AI Agents, or similar GenAI orchestration frameworks.
• Must have experience developing and managing high-volume APIs using FastAPI, Flask, or similar Python frameworks.
• Must have exposure to vector databases and semantic search technologies such as Pinecone, FAISS, Weaviate, Elasticsearch, or similar platforms.
• Must have experience with Docker, Git, CI/CD pipelines, and deploying applications in production environments.
• Must have exposure to LLMOps/MLOps practices, including model monitoring, evaluation, versioning, and production support.
• Must possess strong problem-solving skills, Root Cause Analysis (RCA) capabilities, and the ability to independently troubleshoot complex technical issues.
• Experience with traditional Machine Learning algorithms and predictive modeling.
• Exposure to Azure OpenAI Services, Azure Kubernetes Service (AKS), or other cloud AI platforms.
• Experience working with PostgreSQL, MongoDB, Redis, Kafka, and scalable distributed systems.
• Candidates from AI-first startups, GenAI companies, SaaS product companies, Fintechs, Banking Technology organizations, or data-driven product companies.