Data Scientist ( Machine learning, NLP and GenAI )

Job Description

About the Role: We are seeking a highly motivated and skilled Data Scientist with 25 years of industry experience in Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI (GenAI). The ideal candidate will have a strong foundation in Python programming and a passion for solving complex business problems using data. You will work on designing, developing, and deploying scalable AI solutions for real-world applications across domains. This is a hands-on role requiring technical expertise, experimentation, and collaboration across cross-functional teams. Key Responsibilities: Perform Exploratory Data Analysis (EDA) to uncover patterns, insights, and anomalies. Design, build, and optimize ML and DL models using Python for diverse business use cases. Develop and apply NLP techniques for text analysis and language understanding. Build, fine-tune, and deploy Generative AI solutions (LLMs, VLMs, RAG) tailored to enterprise needs. Create and maintain end-to-end ML pipelines , including data preprocessing, feature engineering, training, evaluation, and deployment. Manage and monitor models in production using MLOps workflows (CI/CD, model tracking, versioning, etc.). Stay updated with recent developments in AI/ML and continuously explore new tools and frameworks. Required Skills & Qualifications: Strong programming proficiency in Python is essential. Experience with Python libraries such as pandas, NumPy, scikit-learn, NLTK, Transformers (Hugging Face), TensorFlow/PyTorch . Solid understanding of machine learning algorithms, statistical modeling, and deep learning concepts. Hands-on experience in Natural Language Processing and Generative AI techniques (e.g., LLMs, RAG, VLMs, Agents). Experience with EDA, data preprocessing , and feature engineering. Familiarity with MLOps tools like MLflow, DVC or similar. Experience with Docker and containerized deployment of ML models. Working knowledge of databases both SQL and NoSQL . Experience deploying ML models in production environments . Familiarity with version control tools (e.g. Git) and collaborative workflows. Exposure to cloud platforms like AWS, GCP, or Azure is a plus.