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Veneel K. from India

Veneel K.

India No experience yet
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About

Veneel K. is an accomplished AI/ML-focused Software Engineer with extensive experience in building backend services and AI features, including REST APIs, microservices, GenAI applications, and RAG pipelines using LLMs and vector databases. He demonstrates proficiency in Python, FastAPI, and AWS/Azure cloud platforms, which he has leveraged in roles such as an AI/ML Engineer during a project-based internship at Rooman Technologies. His work there involved developing a high-accuracy Heart Disease Prediction model using Random Forest and Gradient Boosting. As a professional, Veneel has engineered complex data processing solutions like the Agentic Data Pipeline Reliability Platform, utilizing Docker for scalable backend service deployment. Additionally, he has crafted a RAG-based Knowledge Assistant integrating HuggingFace embeddings for precise context retrieval. An author in his field, Veneel published research on Alzheimer's diagnosis using deep learning, achieving significant accuracy benchmarks. He holds a B.E. in AI & Data Science from KSSEM, Bangalore, and is Microsoft Azure AI-900 certified.

Experience

  • Project Contributor

    CrewAI · 2026 — Present
    Designed a CrewAI orchestration pipeline featuring five agents for tasks such as failure classification and root cause analysis while implementing RAG-powered documentation retrieval and generating reports with FastAPI, ChromaDB, and Docker for deployment.
  • AI/ML Engineer — Project-Based Internship

    Rooman Technologies · 2024 — 2025
    Developed and optimized ML models including classification, regression, and ensemble techniques using Python and Scikit-Learn during an AI/ML internship, with a key project focused on Heart Disease Prediction through an ensemble-based solution, notably utilizing Random Forest and Gradient Boosting.
  • Project Contributor

    null · 2025 — 2025
    Created a comprehensive RAG application that facilitated semantic, document-aware question-answering by integrating HuggingFace LLM embeddings and utilizing ChromaDB for context retrieval, delivered through a FastAPI REST backend and a Streamlit user interface.
  • Project Contributor

    null · 2025 — 2025
    Engineered an end-to-end ML pipeline for predicting customer churn on the IBM Telco dataset, focusing on feature engineering and model benchmarking utilizing Python, Scikit-Learn, and Power BI.

Skills & Expertise

Education

  • B.E. (Engineering) in AI & Data Science
    KSSEM, Bangalore · 2021 — 2025

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