Gen AI and Data Scientist

About The Role

Gen AI & Data Science Lead

NLP, Generative AI, Machine Learning, and Python

Key Responsibilities

• Lead end-to-end development of Generative AI and NLP solutions, including model design, training, fine-tuning, and deployment.
• Architect and implement machine learning models, ensuring accuracy, scalability, and performance.
• Build and maintain pipelines for data processing, model training, inference, and production deployment.
• Work with large-scale datasets and develop advanced NLP models (LLMs, transformers, embeddings, RAG systems, etc.).
• Evaluate and integrate modern GenAI frameworks (LangChain, LlamaIndex, HuggingFace, etc.).
• Collaborate with product, engineering, and business teams to identify AI-driven opportunities and deliver high-impact solutions.
• Lead and mentor a team of data scientists, ML engineers, and analysts.
• Stay updated with the latest advancements in LLMs, generative AI, deep learning, and MLOps practices.
• Conduct POCs, research new AI capabilities, and drive innovation culture across projects.
• Present insights, model performance, and solution recommendations to leadership and stakeholders.
  

Mandatory Skills

• Strong hands-on experience in Natural Language Processing (NLP)
• Expertise in Generative AI, LLMs, and transformer-based architectures
• Solid understanding of Machine Learning (ML) algorithms and model lifecycle
• Advanced proficiency in Python and ML libraries (TensorFlow, PyTorch, HuggingFace, Scikit-learn, etc.)
• Experience building AI models for text generation, summarization, classification, NER, sentiment analysis, and conversational AI
• Familiarity with cloud platforms (AWS/Azure/GCP) for AI/ML development
• Strong understanding of data preprocessing, vector databases, embeddings, and RAG frameworks
  

Preferred / Good-to-Have Skills

• Experience with MLOps: MLflow, Kubeflow, Airflow, Docker, CI/CD automation
• Knowledge of big data technologies (Spark, Databricks)
• Experience deploying AI models in production environments
• Understanding of data engineering workflows
• Experience in leading cross-functional teams and managing AI projects