Venkatesh Vemani

AI/ML Engineer & Data Scientist with 10+ years of experience building scalable ML, LLM, and data engineering solutions. Expert in Python, SQL, Spark, TensorFlow, PyTorch, Keras, Hugging Face, LangChain, and vector databases. Strong in LLM engineering: RAG, hybrid retrieval, Google Cloud LLMs, Google Vector Search, LoRA/PEFT fine-tuning, and model optimization. Data Scientist specializing in Generative Al, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) with expertise in building enterprise Al solutions for intelligent automation and knowledge discovery. Experienced in OpenAI APIs, LangChain, Hugging Face Transformers, and vector databases for scalable Al applications. Designed and implemented RAG architectures with FAISS and Pinecone vector databases, enabling semantic search, contextual document retrieval, and conversational Al systems. Experienced in designing and deploying Al-driven conversational applications using LLMs and Google Cloud Al services (Dialogflow/ Conversational Al Suite). Hands-on experience designing and deploying Al/ML solutions on Microsoft Azure, leveraging Azure Machine Learning, Azure OpenAl, and cloud-native services. Strong experience working with big data technologies such as Apache Spark and Hadoop for large-scale data processing. Hands-on experience with cloud platforms including AWS and Google Cloud Platform (GCP) for building and deploying AI/ML solutions, Proficient in machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn for developing predictive models. Strong expertise in building secure, scalable, and reliable cloud-native architectures using Azure compute, storage, and networking services. Proven ability to integrate Al capabilities into enterprise applications and data platforms using REST APIs and microservices. Strong expertise in prompt engineering and LLM optimization, improving response accuracy and contextual relevance Proven ability to integrate AI/ML models with enterprise systems via REST APIs and microservices architecture Hands-on experience building scalable Al solutions using Python on Google Cloud Platform (GCP). Experienced in building data visualization dashboards using Tableau and Power BI to deliver actionable business insights Developed and fine-tuned LLM and machine learning models using Python, TensorFlow, PyTorch, and Scikit-learn for forecasting, recommendation engines, and intelligent chatbot assistants. Built end-to-end Al pipelines for model training, validation, deployment, and monitoring using MLflow, Apache Airflow, Docker, and Kubernetes for scalable production environments. Applied Natural Language Processing (NLP) techniques including NER, text classification, sentiment analysis, and topic modeling using spaCy and transformer-based architectures. Developed interactive Al applications and dashboards using Streamlit and FastAPI, enabling secure conversational access to enterprise knowledge systems. Implemented MLOps practices with CI/CD pipelines, containerization, and model monitoring, improving deployment efficiency and ensuring reliable Al model lifecycle management. Designed data pipelines and data lake architectures supporting large-scale Al training datasets, integrating distributed processing frameworks such as Apache Spark and Dask. Applied Explainable AI (SHAP, LIME) and statistical modeling techniques to improve model interpretability and support data- driven business decision making. Collaborated with cross-functional teams including data engineers, product managers, and business stakeholders to translate complex business problems into scalable Al and analytics solutions. Data Scientist specializing in Generative Al, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) with proven experience designing enterprise Al solutions for intelligent automation, knowledge discovery, and conversational Al. Extensive expertise in OpenAI APIs, LangChain, Hugging Face Transformers, and vector databases (FAISS, Pinecone) to build scalable semantic search, chatbot, and document intelligence platforms. Strong background in machine learning and deep learning using Python, TensorFlow, PyTorch, and Scikit-learn, developing predictive analytics, recommendation systems, and intelligent automation solutions. Experienced in building end-to-end AI/ML pipelines and MLOps workflows using MLflow, Apache Airflow, Docker, Kubernetes, and CI/CD pipelines for scalable and production-ready deployments. Proficient in Natural Language Processing (NLP) and embedding-based architectures, implementing NER, sentiment analysis, topic modeling, and contextual search systems using transformer-based models. Skilled at translating complex business problems into Al-driven solutions, collaborating with cross-functional teams to deliver scalable analytics platforms and improve operational efficiency through data-driven decision making.