Data Scientist AI/ML

Remote (US Shift) | Full Time | 4+ years of experience (minimum) Requirements Strong programming in Python with hands-on Django/Flask experience. Expertise in Machine Learning, Deep Learning, NLP, and Computer Vision (TensorFlow/PyTorch, scikit-learn). Familiarity with Generative AI, LangChain, RAG, LLMs preferred. Knowledge of SQL/NoSQL databases , API development , and data visualization (Power BI/Tableau). Experience with MLOps/DevOps tools Docker, Kubernetes, MLflow, Git, Azure. Strong analytical, problem-solving, and communication skills. Degree in Data Science, Computer Science, or related field (Masters preferred). Responsibilities Develop, deploy, and maintain AI/ML models using Python and Django. Build scalable data pipelines and APIs integrating ML models into production. Work across domains such as NLP, Computer Vision, and Generative AI for real-world solutions. Perform data preprocessing, model training, and performance tuning . Collaborate with cross-functional teams for AI product development and insights. Automate workflows and ensure robust, reproducible, and efficient ML systems. Implement MLOps practices (Docker, MLflow, Kubernetes, Azure). Document model architectures, maintain version control, and support deployment. Remote (US Shift) | Full Time | 4+ years of experience (minimum) Requirements Strong programming in Python with hands-on Django/Flask experience. Expertise in Machine Learning, Deep Learning, NLP, and Computer Vision (TensorFlow/PyTorch, scikit-learn). Familiarity with Generative AI, LangChain, RAG, LLMs preferred. Knowledge of SQL/NoSQL databases , API development , and data visualization (Power BI/Tableau). Experience with MLOps/DevOps tools Docker, Kubernetes, MLflow, Git, Azure. Strong analytical, problem-solving, and communication skills. Degree in Data Science, Computer Science, or related field (Masters preferred). Responsibilities Develop, deploy, and maintain AI/ML models using Python and Django. Build scalable data pipelines and APIs integrating ML models into production. Work across domains such as NLP, Computer Vision, and Generative AI for real-world solutions. Perform data preprocessing, model training, and performance tuning . Collaborate with cross-functional teams for AI product development and insights. Automate workflows and ensure robust, reproducible, and efficient ML systems. Implement MLOps practices (Docker, MLflow, Kubernetes, Azure). Document model architectures, maintain version control, and support deployment.