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    "name": "Sourabh Bhandary",
    "label": "Data Engineer",
    "summary": "Python Developer and Data Automation Engineer with around 5 years of experience in backend development, focusing on ETL pipeline development using PySpark, building RESTful APIs for enterprise environments. Skilled in Python, FastAPI, Pandas, SQL/NoSQL databases, and effective application monitoring. Proven ability to collaborate across teams to enhance operational effectiveness and provide impactful data analytics and insights.",
    "location": "Bangalore, India",
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    {
      "company": "SRS Business Solutions Pvt. Ltd. (India)",
      "position": "Python Developer",
      "startDate": "2021-02",
      "endDate": "2022-05",
      "highlights": [
        "Designed and developed production-ready Python pipelines for ETL processes, integrating workflows that enhance Feature Engineering capabilities for Data Scientists.",
        "Built worksheets workflow automation with UiPath for Cisco billing sheets for EMEA region",
        "Engaged with cross-functional teams in Agile/Scrum sessions for project planning and estimation, improving delivery timelines and collaboration for client reporting solutions."
      ]
    },
    {
      "company": "Akamai Technologies",
      "position": "Software Engineer",
      "startDate": "2022-06",
      "endDate": "2023-08",
      "highlights": [
        "Built customer-facing dashboards using Python and robust visualization tools, ensuring clear data representation for advertisers and improving their insights into customer behaviors while facilitating seamless user experiences across platforms.",
        "Collaborated in the implementation of Data collection and preprocessing with PySpark to maintain data integrity and provide real-time insights, increasing operational efficiency and allowing for swift corrections.",
        "Streamlined processed data  integration and reporting, allowing for quicker adaptations to new clients' needs, thus improving responsiveness on reporting requests."
      ]
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    {
      "company": "Eximietas Design | Sandisk",
      "position": "Software Engineer",
      "startDate": "2025-01",
      "endDate": "2026-07",
      "highlights": [
        "Assisted deployment and validation of machine learning solutions on production environments, supporting application monitoring and automating data processes to ensure ongoing performance improvements.",
        "Developed Python-based data pipeline modules that facilitated rapid collection and processing of telemetry data for SSD firmware, achieving significant improvements in testing efficiency for advertising solutions.",
        "Contributed to cross-team projects focusing on data-driven applications for advertising, enhancing overall client outcomes and ensuring a higher standard of data accuracy and reporting reliability."
      ]
    }
  ],
  "education": [
    {
      "institution": "University of Bristol",
      "degree": "MSc",
      "field": "Aerial Robotics",
      "startDate": "2023",
      "endDate": "2024"
    },
    {
      "institution": "Vivekananda Global University",
      "degree": "B.Tech",
      "field": "Computer Science & Engineering",
      "startDate": "2016",
      "endDate": "2019"
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    "GitHub",
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    "Jira",
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    "MySQL",
    "MongoDB",
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      "language": "Bengali",
      "fluency": "Native"
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      "language": "English",
      "fluency": "Fluent"
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    {
      "name": "ANPR",
      "description": "ANPR - Engineered a car number plate recognition system collaborating with a teammate, using YOLOv8, OpenCV, EasyOCR, HuggingFace and Kaggle for text extraction in challenging environment. Focused on Model training, Pipeline optimization, Deployment, Data handling and monitoring using Azure MLOps workstation.",
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    {
      "name": "Smart Retail Demand Forecasting",
      "description": "Designed and productionized a retail demand forecasting pipeline with versioned model artifacts and reproducible training; exposed the model as a FastAPI microservice, containerized it with Docker, and deployed it to Azure Container Apps on Linux using Azure Container Registry; implemented end‑to‑end MLOps practices with GitHub Actions CI/ CD to run tests, build and push images, automatically deploy updates on main‑branch merges, and support ongoing operations via prediction logging, validation metrics tracking, and an on‑demand retraining workflow.",
      "url": "https://github.com/kibutsuji-muzan07/smart-retail-forcasting",
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        "FastAPI",
        "Docker",
        "Azure Cloud"
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