Job
Description
Design, develop, and deploy AI/NLP solutions to solve diverse business challenges—particularly in areas like text classification, information extraction, summarization, and semantic search Conduct exploratory data analysis and feature engineering Contribute to the development initiatives in the GenAI domain, focusing on cutting-edge technologies like Large Language Models, Retrieval-Augmented Generation, and autonomous agents. Validate and monitor solution quality using real-world feedback data Work closely with ML engineers and DevOps teams to operationalize models (on cloud and on-prem environments) Hands-on experience on deploying solutions to cloud-native AI platforms (AWS/Azure/GCP) Collaborate with clients and business stakeholders to scope and refine requirements, validate model behavior, and ensure successful deployment Explore and experiment with LLMs, prompt engineering, and retrieval-augmented generation (RAG) techniques for advanced use cases Contribute to building reusable components, best practices, and scalable frameworks for AI delivery Experience of development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques. Qualifications 3–6 years of hands-on experience in data science, with a focus on NLP, deep learning, and machine learning applications Strong programming skills in Python; experience with relevant libraries such as scikit-learn, spaCy, NLTK, PyTorch, TensorFlow, or Hugging Face Proven experience in delivering NLP/LLM-based solutions Familiarity with cloud platforms (AWS, Azure, or GCP) and experience with deploying AI models to production Ability to handle end-to-end ownership of solutions, from POC to deployment Prior experience in consulting or client-facing data science roles is a plus Exposure to document databases (e.g., MongoDB), graph databases, or vector databases (e.g., FAISS, Pinecone) is a bonus Show more Show less
