Data Scientist

Responsibilities: 
• 10+ years Proficiency in Python and experience with Lang chain, PySpark, PyTorch, TensorFlow, Streamlit and other relevant tools
• Prompt Engineering and AI Chatbot Acumen. 
• Developing sophisticated AI chatbot solutions, including QA/chatbots, translation, and 
• 5+ years of experience in GenAI Foundation Models and Vector DB
• Leveraging foundational AI models and vector database technologies (multi vector) for advanced AI capabilities.
• 5+ years of experience in RAG (Retrieval-Augmented Generation) and Model Fine Tuning: Employing RAG techniques for enhanced AI responses and fine-tuning AI models for optimal performance.
• Use of Orchestration Tools: Utilizing advanced tools like Semantic Kerner, Lang chain, and others for efficient AI model management.
• Expertise in either OpenAI or Google Vertex and/or other models from Hugging face for implementing advanced language models.
• Managing high availability and efficient deployment of cloud-native applications
• Experience with hosting LLMs on-premises.
• Experience with GitOps principles and tools, such as Git, Tekton, Flux, or ArgoCD, for managing infrastructure and application deployments.
• Strong problem-solving skills and the ability to work in a collaborative and fast-paced environment.
• 5+ years of experience in Machine Learning / AI
• Bachelor’s or master’s degree in computer science, AI, or a related field
• This role is crucial in achieving the best RAG results on client data and contributing to the broader AI and machine learning community.
• Collaborate with cross-functional teams to understand business requirements and design AI solutions based on language models.
• Develop, train, and optimize language models using PyTorch, TensorFlow, and other relevant frameworks.
• Utilize expertise in either OpenAI or Google Vertex to implement state-of-the-art language models.
• Responsible for End-to-End RAG Solution Design and Development
• Lead the experimentation of different chunking and retrieval methods to enhance the efficiency and effectiveness of RAG systems.
• Developing, training, fine-tuning LLMs for RAG
• Conduct thorough evaluations of model and application performance.
• This entails a deep dive into model accuracy, bias identification, and the formulation of strategies for ongoing enhancement.
• Analytical skills that drive continuous improvements and set new benchmarks for excellence.
• Engage with customers from various sectors to facilitate the successful adoption of Unstructured APIs