Venkat Vinay Randhi
Data Scientist
Visakhapatnam, IN.About
Highly accomplished Data Scientist specializing in Machine Learning, Retrieval-Augmented Generation (RAG), and MLOps, with a strong focus on deploying scalable AI solutions. Proven ability to translate complex data into actionable insights, driving innovation and efficiency across diverse applications including demand forecasting and intelligent system design. Leverages expertise in Python, advanced SQL, and cloud platforms (Azure, AWS) to develop robust, data-driven solutions and automate intricate business workflows.
Work
Hyderabad, Telangana, India
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Summary
Led the development and deployment of advanced AI/ML solutions, including RAG applications and demand forecasting models, to drive client-specific strategic outcomes.
Highlights
Led the development and deployment of a Retrieval-Augmented Generation (RAG) application using LightRAG, integrating scalable vector search pipelines with vector and graph databases to enhance knowledge retrieval and semantic relationships for clients.
Designed and implemented end-to-end demand forecasting models utilizing time series and deep learning techniques for a major retail client, optimizing inventory management and enhancing sales strategies.
Utilized advanced clustering algorithms (K-Means, DBSCAN) to segment customer and product categories, significantly enhancing personalization and targeting capabilities.
Developed and automated AI agents to streamline complex, rules-based business workflows, significantly reducing manual intervention and boosting operational efficiency.
Spearheaded the deployment of robust MLOps architectures using Docker, Kubernetes, MLflow, and Prefect, ensuring high reproducibility, scalability, and automation of critical ML workflows.
Executed a Proof of Concept (POC) utilizing the Google OWLv2 model for advanced image detection, successfully enhancing the system's visual recognition capabilities.
Skills
Machine Learning & AI
Exploratory Data Analysis (EDA), Statistical Analysis & Hypothesis Testing, Supervised & Unsupervised Learning (Clustering, PCA), Time Series Forecasting (ARIMA, Prophet), Demand Forecasting, Retrieval-Augmented Generation (RAG), NLP, Sentiment Analysis, Transformer Models (LLaMA, BERT), Image Recognition (CNNs, OWLv2), Generative Adversarial Networks (GANs), AI Agent Design.
MLOps & Deployment
MLflow, Prefect (Pipeline & Experiment Tracking), Docker, Kubernetes (Model Containerization & Deployment), Vector Databases, Graph Databases, Databricks, Azure Cloud, AWS, Git, CI/CD Workflows.
Programming & Tools
Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Visualization libraries), SQL (Advanced Data Querying & Joins).