Lead Data Scientist - NLP/GenAI in Bangalore, भारत

Role Overview : We are looking for a Lead Data Scientist to own and drive AI/ML initiatives across NLP, Traditional ML, and Generative AI. The role will focus heavily on data problem formulation, training data strategy, model training, and building production-grade AI systems that continuously improve from real-world https://jobeax.com/link/KPSLG15VVkQhI5am will work closely with Data Engineering, GenAI/NLP, and Product teams to build scalable AI solutions and take ownership of technical architecture, model development, and AI/ML best https://jobeax.com/link/UeihKekmHfcz25oy You'll Do : - Own data problem formulation for AI systems, including structuring, labeling, and mining historical decision data and domain signals.- Lead training data strategies by building high-quality labeled datasets from real-world data and prioritizing annotation efforts.- Drive large-scale data mining across high-cardinality datasets to identify rare but valuable patterns.- Own the architecture and evolution of AI platforms covering real-time inference, batch processing, and model training pipelines.- Build multi-modal AI systems combining unstructured text, structured data, and domain knowledge.- Lead technical evaluation of AI tools, platforms, and vendors, including build-vs-buy decisions.- Mentor AI engineers across NLP, Traditional ML, and GenAI and lead architecture reviews.- Collaborate with Data Engineering, GenAI/NLP, and Product teams to align data and model training strategies with product https://jobeax.com/link/UMubPr2lWLuuPE8z We're Looking For : - 6+ years of experience building and deploying production AI/ML systems, with strong emphasis on solving data problems.- Deep experience working with complex, domain-specific data at scale.- Strong expertise across multiple AI domains, including NLP, Traditional ML, and GenAI.- Expert-level proficiency in PyTorch or TensorFlow, with hands-on experience in model training at scale.- Proven experience where data quality, labeling strategy, and training data curation were key challenges. (ref:hirist.tech)