Job
Description
Lead development initiatives in the GenAI domain, focusing on cutting-edge technologies like Large Language Models, Retrieval-Augmented Generation, and autonomous agents. Design and implement advanced workflows for integrating LLMs into real-world applications across various domains such as Finance, Insurance, and Healthcare. Develop and fine-tune domain-specific LLMs to optimize performance, using techniques like prompt engineering, adapter-based tuning, or low-rank adaptation. Architect and implement scalable solutions to enhance reasoning capabilities in AI systems. Drive the development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques. Collaborate with cross-functional teams to prototype, test, and deploy solutions in production environments, both on-premise and in the cloud. Mentor and guide junior data scientist to foster innovation and ensure high-quality deliverables. Stay at the forefront of AI advancements by reading, adapting, and implementing cutting-edge research to solve real-world challenges. Document research findings, methodologies, and implementations for internal and external stakeholders. Qualifications 4–8 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
