GenAI Data Scientist

About the Role

Key Responsibilities

• · Develop and deploy end-to-end ML/AI pipelines , from data collection, preprocessing, and feature engineering to model training, evaluation, and deployment.
• · Apply traditional ML/AI techniques (regression, classification, clustering, recommendation systems, optimization).
• · Design and implement GenAI solutions using large language models (LLMs) , transformers , and multimodal architectures (text, image, audio).
• · Fine-tune, evaluate, and optimize foundation models (e.g., GPT, LLaMA, Claude, Falcon, Stable Diffusion) for enterprise use cases.
• · Research, experiment, and prototype cutting-edge AI solutions tailored to business problems.
• · Collaborate with data engineers, ML engineers, and domain experts to productionize models and integrate them into business workflows.
• · Containerize and orchestrate models and pipelines (Docker, Kubernetes).
• · Stay updated with the latest trends in AI, ML, and GenAI, and drive innovation and adoption of new methods .

Required Qualifications:

• · Bachelor’s degree (B.Tech, BE, or equivalent) in Computer Science, Data Science, AI, or related field.
• · Advanced degree (M.Tech/MS/MBA/PhD) is a plus but not mandatory.
• · 3-5 years of experience in ML/AI, with hands-on expertise in building and deploying models.
• · Functional expertise in at least one business domain (e.g., Supply Chain, Finance, Healthcare, Pharma, Customer Analytics, Manufacturing).
• · Proficiency in Python, PyTorch/TensorFlow, scikit-learn, Hugging Face, LangChain.
• · Strong understanding of NLP, predictive modeling, deep learning architectures (CNNs, RNNs, Transformers).
• · Experience with cloud AI platforms (AWS, Azure, GCP) and MLOps tools (Docker, Kubernetes, CI/CD).
• · Strong stakeholder management and ability to communicate technical insights in business terms.