Data Scientist

Role and Responsibilities

• Develop and maintain end-to-end pipelines for image processing and OCR, including designing, training, and optimizing machine learning models for OCR, text extraction, and image classification.
• Implement advanced image preprocessing techniques such as denoising, resizing, normalization, and feature extraction to improve system accuracy and robustness.
• Leverage OCR technologies (open-source and commercial, including Google Vision, Tesseract, and Azure OCR) to enhance performance and reliability across diverse datasets.
• Perform large-scale data processing using tools like OpenCV, TensorFlow, and PyTorch, ensuring scalable and efficient pipelines.
• Collaborate with data engineers, software developers, and product managers to integrate OCR and AI-driven solutions into production systems.
• Conduct research to stay at the forefront of advancements in image processing, OCR, and AI technologies, ensuring continuous innovation.
• Analyze the performance of OCR models and implement enhancements with a focus on metrics like accuracy, speed, and robustness.
• Design and execute experiments to evaluate and benchmark model performance on diverse and complex image datasets.
• Architect and develop Retrieval-Augmented Generation (RAG) pipelines for advanced knowledge-based systems, leveraging cutting-edge tools like LangChain, LangSmith, and SQL databases.
• Build and maintain conversational AI systems that integrate text, audio, and image inputs using technologies such as OpenAI Whisper, CLIP, and HuggingFace Embeddings.
• Design scalable and efficient backends for generative AI applications using frameworks like Flask and FastAPI, and deploy them on cloud platforms including AWS, Azure, or GCP.
• Integrate multimodal capabilities into chatbot systems, enabling seamless processing and response generation for text, image, and audio inputs.
• Research and implement state-of-the-art techniques in generative AI, including the development and deployment of large/small language models to power innovative solutions.
• Ensure system scalability, performance, and reliability through robust MLOps practices, including CI/CD pipelines, Docker, and GitHub Actions, while maintaining a focus on efficiency and production readiness.