Solid knowledge of Generative AI concepts including foundation models / LLMs, embeddings, and NLP fundamentals used in GenAI solutions.
Design, implement, and optimize GenAI applications (LLM-powered services) for real business use-cases, with focus on accuracy, efficiency, and scalability.
Hands-on prompt engineering (e.g., zero/few-shot, Chain-of-Thought, Tree-of-Thought, prompt templates) and ability to evaluate and refine prompts based on output quality.
Build Retrieval-Augmented Generation (RAG) solutions, including retrieval strategy integration and prompt + retrieval orchestration patterns.
Experience with agent / app evaluation practicesevaluate model and agent performance for GenAI use-cases (quality, effectiveness, benchmarking).
Cloud-based GenAI platform experience (e.g., AWS / Azure / GCP) for building and deploying scalable GenAI solutions with high availability and performance.
MLOps / LLMOps-aligned practices: CI/CD, monitoring, logging, and version control for deploying and operating GenAI systems in production.
Proficiency in programming languages such as Python, .Net or Java, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).

📌 Gen Ai Data Scientist Bengaluru (India)
🏢 Tata Consultancy Services
📍 India