resume.json

{
  "basics": {
    "name": "Omkar Vijay Bhad",
    "label": "Machine Learning Engineer",
    "email": "omkarbhad.work@gmail.com",
    "phone": "2167721518",
    "url": "https://www.linkedin.com/in/omkarbhad/",
    "summary": "Machine Learning Engineer with strong foundations in data science, generative AI, and scalable backend systems. Proven experience in Python, SQL, cloud infrastructure (GCP, AWS), and deploying ML models that influence product strategy and user behavior. Skilled in developing RESTful APIs, experimenting with LLMs, and delivering insights through statistical analysis and data storytelling. Passionate about shaping AI-driven SaaS products with business impact.",
    "location": {
      "city": "Cleveland",
      "region": "Ohio",
      "countryCode": "US"
    },
    "profiles": [
      {
        "network": "LinkedIn",
        "username": "omkarbhad",
        "url": "https://www.linkedin.com/in/omkarbhad/"
      }
    ]
  },
  "work": [
    {
      "name": "Ugam Merkle",
      "position": "Associate Software Engineer",
      "startDate": "2022-04",
      "endDate": "2022-07",
      "summary": "Developed scalable scraping systems and data pipelines supporting enterprise analytics. Focused on data reliability, system observability, and cloud-native performance optimization.",
      "highlights": [
        "Built and deployed 200+ production scraping pipelines using Python and Docker across cloud platforms",
        "Led Python 2 to 3 migration with unit test coverage and CI pipeline refactoring",
        "Integrated email alerting and failure recovery logic to improve operational uptime",
        "Worked on data readiness for BI and machine learning integration in enterprise systems"
      ]
    },
    {
      "name": "iSmile Technologies",
      "position": "AI / Data Scientist Intern",
      "startDate": "2020-08",
      "endDate": "2020-11",
      "summary": "Led a team to deploy ML-based inference systems on GCP. Built RESTful APIs using Django for business applications and conducted analytics on real-time vehicle telemetry data. Contributed to ML model experimentation and business-focused insights.",
      "highlights": [
        "Deployed predictive ML models using cloud-native infrastructure (GCP, Docker, Django)",
        "Built dashboards and visualization tools using Tableau and Python for operational intelligence",
        "Conducted statistical modeling (e.g., regressions, A/B tests) for feature effectiveness",
        "Analyzed and presented insights to improve vehicle maintenance strategies and user experience"
      ]
    }
  ],
  "education": [
    {
      "institution": "Cleveland State University",
      "area": "Computer Science",
      "studyType": "Masters",
      "startDate": "2022-08",
      "endDate": "2025-05"
    },
    {
      "institution": "Pune University",
      "area": "Mechanical Engineering",
      "studyType": "Bachelor",
      "startDate": "2015",
      "endDate": "2020"
    }
  ],
  "awards": [
    {
      "title": "2nd Place - Report a Security Vulnerability Competition",
      "date": "2021-09",
      "awarder": "ISMS Team, Ugam Merkle"
    },
    {
      "title": "Individual Award for Innovation",
      "date": "2021-12",
      "awarder": "Navin Dhananjaya, Chief Solutions Officer (Ugam Merkle)"
    }
  ],
  "certificates": [
    {
      "name": "Data Science Nanodegree",
      "date": "2020-07",
      "issuer": "Udacity",
      "url": "https://confirm.udacity.com/NCZLGPEA"
    },
    {
      "name": "Post Graduation in Data Analytics",
      "date": "2020-10",
      "issuer": "Imarticus Learning"
    }
  ],
  "projects": [
    {
      "name": "AI-assisted Vehicle Maintenance",
      "startDate": "2020-08",
      "endDate": "2020-11",
      "description": "Led predictive maintenance project using CatBoost and LightGBM, achieving 89% accuracy. Applied statistical techniques like regression and clustering. Deployed ML models via REST APIs for product integration."
    },
    {
      "name": "Anti-Plagiarism and Grading System",
      "startDate": "2020-06",
      "endDate": "2020-06",
      "description": "Developed full-stack Django app that grades essays using NLP models trained on 12,000+ samples. Applied LLM-inspired architecture for scoring logic and deployed in cloud environment."
    },
    {
      "name": "Mobile App Data Scraping System",
      "startDate": "2021-11",
      "endDate": "2022-01",
      "description": "Designed data scraping pipeline with Kafka, Docker, Appium, and OpenSTF. Reduced system latency by 40% and enabled data acquisition for ML product experimentation."
    },
    {
      "name": "Scalable MLOps Platform for Automated Model Training & Deployment",
      "startDate": "2023-01",
      "endDate": "2024-04",
      "description": "I architected and led the end-to-end development and rollout of a robust, cloud-native MLOps platform designed to automate the full lifecycle management of machine learning models focused on IT reliability and operations optimization. My solution streamlined workflows encompassing data ingestion, feature engineering, model training, validation, deployment, and continuous monitoring — all critical to supporting high-availability IT systems.\n\nUsing AWS SageMaker for managed model training and hosting, Terraform for infrastructure as code, and Kubeflow pipelines to orchestrate complex workflows, I built repeatable and scalable processes that handled large-scale telemetry and operational data seamlessly. I engineered modular data preprocessing components leveraging Python and PyTorch, alongside classical ML model development with scikit-learn, to provide flexibility for both deep learning and traditional algorithms.\n\nTo ensure production readiness, I designed zero-downtime deployment strategies incorporating blue/green and canary release methodologies integrated into CI/CD pipelines orchestrated with Jenkins. This ensured smooth rollout of model updates without service disruption. I established rigorous code review standards and best practices within the team, driving maintainable, secure, and high-quality code across ML components.\n\nFor observability, I integrated Prometheus metrics and Grafana dashboards to provide real-time insights into pipeline health, model performance drift, and infrastructure utilization, enabling proactive maintenance and troubleshooting.\n\nThroughout the project, I led collaboration with cross-functional teams including data engineers, SREs, and product owners to align platform capabilities with operational needs, accelerating feature delivery cycles. This MLOps platform empowered rapid experimentation and production deployment of over 20 distinct ML models, resulting in a 25% reduction in model drift-related incidents, significantly improving system reliability and IT operational efficiency."
    }
  ],
  "skills": [
    {
      "name": "Programming Languages",
      "keywords": [
        "Python",
        "SQL",
        "JavaScript",
        "R"
      ]
    },
    {
      "name": "ML & Data Science",
      "keywords": [
        "Machine Learning",
        "Generative AI",
        "LLM",
        "TensorFlow",
        "Scikit-learn",
        "CatBoost",
        "LightGBM",
        "NLP",
        "Pandas",
        "NumPy",
        "Statistical Modeling",
        "A/B Testing",
        "Clustering"
      ]
    },
    {
      "name": "Cloud & Deployment",
      "keywords": [
        "GCP",
        "AWS",
        "Docker",
        "CI/CD",
        "RESTful APIs"
      ]
    },
    {
      "name": "Data Analytics & Visualization",
      "keywords": [
        "Data Storytelling",
        "Tableau",
        "Matplotlib",
        "Dashboards",
        "Business Analytics"
      ]
    },
    {
      "name": "Product & SaaS Understanding",
      "keywords": [
        "SaaS Metrics",
        "Experimentation",
        "User Behavior Analysis",
        "Feature Measurement",
        "Product Impact"
      ]
    }
  ],
  "languages": [],
  "interests": [],
  "references": []
}