Python-Data Science - AI/ML

• Design, develop, and deploy machine learning models in production environments
• Build and optimize NLP, LLM, and Generative AI solutions
• Develop Retrieval-Augmented Generation (RAG) based applications
• Work with large datasets to perform EDA, feature engineering, and hypothesis testing
• Collaborate with cross-functional teams to integrate ML models into business systems
• Deploy and manage ML models on cloud platforms (Azure ML / AWS SageMaker)
• Ensure scalability, performance, and reliability of AI/ML solutions
• Create data pipelines and workflows for model training and inference
• Develop dashboards and visualizations to communicate insights

We are seeking a highly skilled Python Data Scientist with strong expertise in AI/ML to design, build, and deploy scalable machine learning solutions. The ideal candidate will have hands-on experience in production-grade ML systems, NLP, LLMs, and Generative AI, along with a strong foundation in data science and cloud-based ML platforms.

This role involves working on cutting-edge AI solutions, including RAG-based applications, and contributing across the full lifecycle-from data exploration to model deployment., * Strong experience in Python for Data Science and Machine Learning

• Hands-on experience in ML model deployment in production
• Expertise in NLP, LLMs, and Generative AI
• Experience building RAG-based applications
• Proficiency in PyTorch or TensorFlow
• Experience with cloud ML platforms (Azure ML / AWS SageMaker)

Secondary Skills (Good to Have)

• Exposure to Reinforcement Learning
• Strong understanding of EDA & statistical hypothesis testing
• Experience with Spark ML / distributed computing
• Knowledge of SQL / NoSQL databases
• Experience in data visualization tools (Tableau, ELK stack), * Bachelor's or Master's degree in Computer Science, Data Science, AI, or related field
• 3+ years of experience in Data Science / AI-ML roles
• Strong problem-solving and analytical skills

Nice to Have

• Experience working with enterprise-scale AI systems
• Familiarity with MLOps practices and CI/CD pipelines
• Exposure to real-time data processing and model monitoring