Yogesh is a MLOps Engineer with 5+ years of experience in designing and deploying machine learning pipelines. He specializes in real-time object detection and LLM integration, demonstrating strong capabilities in cloud technologies and CI/CD practices.
Successfully developed and deployed real-time ML systems for retail product detection.
Designed and implemented advanced LLM-powered chatbots utilizing LangChain.
Optimized computational efficiency for AI models, utilizing multiple GPUs for inference tasks.
Developed and deployed real-time ML systems for retail product detection and traffic congestion analysis.
Integrated external APIs (Binance) for real-time data processing in an AI Crypto Trading platform.
Led the analysis of customer sentiments for product improvement strategies, providing strategic insights to clients.
Overview: Developed a system for automatic product detection in retail carts using computer vision and ML. Responsibilities: Implemented video processing to capture and identify changes when new products were placed in the cart. Developed image cropping functionality to isolate relevant products for analysis. Integrated a YOLO model for accurate product detection and comparison with scanned barcodes.
Key outcomes:
Ensured accuracy through barcode comparison.
Optimized system for real-time performance.
Overview: Developed a Retrieval-Augmented Generation chatbot leveraging text embeddings and semantic similarity. Responsibilities: Integrated the model with the chatbot's framework, optimizing for efficiency and accuracy. Developed and refined the architecture of the chatbot, ensuring seamless integration with LangChain and LLM.
Key outcomes:
Successfully integrated a Retrieval-Augmented Generation model into a chatbot framework.
Developed a system capable of semantic similarity search.
Overview: Developed object tracking and jam analysis using TensorFlow and OpenCV. Responsibilities: Developed and optimized the codebase for object tracking and jam analysis. Implemented machine learning models for specific object detection.
Key outcomes:
Developed a system for real-time traffic congestion detection.
Implemented and fine-tuned machine learning models for accurate object tracking.
Overview: Developed a cryptocurrency trading platform using AI-driven strategies for generating trade signals. Responsibilities: Developed the backend of the trading platform using Python and Django. Integrated Binance APIs for real-time market data acquisition.
Key outcomes:
Built an AI-driven platform capable of generating cryptocurrency trade signals in real-time.
Integrated external APIs for live market data.
Overview: An NLP project focused on analyzing customer reviews of electronics products. Responsibilities: Analyzed customer reviews to understand sentiments using NLP techniques. Provided strategic insights for client decision-making.
Key outcomes:
Successfully performed sentiment analysis on large volumes of customer reviews.
Provided actionable insights for product development.
Yogesh
Software Engineer