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. - 5. Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models. - 6. Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications. - 7. Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies. - 8. Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies. 3. Compensation & Requirements: - 1. Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy. - 2. Mandatory (Age) - Candidate should be below 28 years. - 3. Mandatory (Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are considered. 4. Preferred Experience: - 1. Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models. - 2. Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems. - 3. Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms. - 4. Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture. - 5. Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.