{
  "$schema": "https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json",
  "basics": {
    "name": "Avaya Aggarwal",
    "label": "ML Engineer / Researcher",
    "email": "aggarwal.avaya27@gmail.com",
    "url": "https://www.avayaaggarwal.com",
    "summary": "I turn ambiguous ML ideas into testable systems: multimodal generation workflows, evaluation tooling, computer-vision research, and backend infrastructure. I care about the evidence behind a result as much as the implementation.",
    "location": {
      "city": "Delhi",
      "countryCode": "IN"
    },
    "profiles": [
      {
        "network": "GitHub",
        "url": "https://github.com/OnePunchMonk"
      },
      {
        "network": "LinkedIn",
        "url": "https://www.linkedin.com/in/avaya-aggarwal/"
      },
      {
        "network": "Kaggle",
        "url": "https://www.kaggle.com/avayaaggarwal27"
      }
    ]
  },
  "work": [
    {
      "name": "Eternal",
      "position": "ML Engineer Intern",
      "location": "Gurugram, India",
      "summary": "Built applied-AI and backend infrastructure for multimodal generation and AI platform operations.",
      "highlights": [
        "Worked on the AI Gateway that connected the platform to language, image, and video models through a common interface for routing, validation, and model configuration.",
        "Built a multimodal marketing-content workflow that took a campaign brief through planning and review to avatar, voice, image, video, and post-production outputs.",
        "Added the supporting backend for asynchronous media processing, retries, persistent state, monitoring, and cost tracking.",
        "Worked on computer-vision and image-generation workflows for food, catalogue, and brand-personalisation use cases.",
        "Built Streamlit demos for creating and editing marketing content and reviewing costs."
      ],
      "keywords": [
        "Python",
        "LLM APIs",
        "SQS",
        "DynamoDB",
        "FFmpeg"
      ],
      "startDate": "Dec 2025",
      "endDate": "Jun 2026"
    },
    {
      "name": "Wells Fargo",
      "position": "Software Engineering Intern",
      "location": "Hyderabad, India",
      "summary": "Built internal tooling around regression validation, mapping workflows, and GenAI-assisted analysis.",
      "highlights": [
        "Reduced QA effort with a format-agnostic regression suite.",
        "Designed a configurable hierarchical JSON mapper backed by 40+ unit tests.",
        "Implemented a query-and-summary workflow for business users to speed up analysis."
      ],
      "keywords": [
        "Python",
        "Test Automation"
      ],
      "startDate": "Jun 2025",
      "endDate": "Aug 2025"
    },
    {
      "name": "Ciena",
      "position": "Software Engineering Intern",
      "location": "Gurugram, India",
      "summary": "Improved internal testing workflows for network systems using Python and APIs.",
      "highlights": [
        "Revamped in-house Python libraries for 10G PON testing.",
        "Integrated REST APIs to improve maintainability and execution efficiency."
      ],
      "keywords": [
        "Python",
        "Test Automation"
      ],
      "startDate": "May 2024",
      "endDate": "Jul 2024"
    }
  ],
  "education": [
    {
      "institution": "Netaji Subhas University of Technology",
      "studyType": "B.Tech in Computer Science Engineering with AI",
      "startDate": "2022",
      "endDate": "2026",
      "courses": [
        "Data Structures and Algorithms",
        "Machine Learning",
        "Artificial Intelligence",
        "Deep Learning",
        "Operating Systems",
        "Database Management Systems",
        "Game Theory"
      ]
    }
  ],
  "projects": [
    {
      "name": "AgentQuant",
      "description": "A failure-aware research harness for proposing, testing, and retaining lessons from quantitative-strategy experiments. Built around a typed ReAct loop, SQLite-backed experiment memory, reproducible runs, and an inspectable offline demo.",
      "url": "https://github.com/OnePunchMonk/AgentQuant",
      "keywords": [
        "Python",
        "LangGraph",
        "SQLite"
      ],
      "entity": "Agentic AI"
    },
    {
      "name": "TradeRecon",
      "description": "A Kafka-driven post-trade reconciliation engine that compares execution, broker confirmation, and PnL records in real time. Flags price, quantity, timestamp, and PnL mismatches with SQLite audit trails, Prometheus metrics, Grafana dashboards, and HTML reports.",
      "url": "https://github.com/OnePunchMonk/TradeRecon",
      "keywords": [
        "Python",
        "Kafka",
        "SQLite",
        "Prometheus"
      ],
      "entity": "Trade reconciliation engine"
    },
    {
      "name": "KoRA",
      "description": "A research exploration of multiplicative inter-adapter communication for parameter-efficient fine-tuning. Includes an explicit formal analysis, counterexamples, tests, and an implementation boundary between the proposal and validated behaviour.",
      "url": "https://github.com/OnePunchMonk/KoRA",
      "keywords": [
        "PyTorch",
        "PEFT",
        "Vision Models"
      ],
      "entity": "PEFT"
    },
    {
      "name": "uois_toolkit",
      "description": "A three-person collaboration with researchers at IRVL, UT Dallas: a unified PyTorch toolkit for unseen-object instance-segmentation datasets, loaders, sampling, augmentation, and evaluation. Standardizes five UOIS datasets behind one API, includes built-in research metrics, and shipped as an installable PyPI package.",
      "url": "https://pypi.org/project/uois-toolkit/",
      "keywords": [
        "Python",
        "PyTorch Lightning"
      ],
      "entity": "Open-source computer vision toolkit"
    },
    {
      "name": "tiny-tribe",
      "description": "A lightweight distilled brain encoding model that predicts fMRI responses to naturalistic video from audio, visual, and text features. Distills TRIBE v2 into a compact multimodal student model with about 14M trainable parameters and reported Pearson r of 0.7278 on the proof of concept.",
      "url": "https://github.com/OnePunchMonk/tiny-tribe",
      "keywords": [
        "Python",
        "PyTorch",
        "Distillation",
        "fMRI"
      ],
      "entity": "Brain encodings"
    },
    {
      "name": "erasus",
      "description": "A machine unlearning framework built around coreset selection, multiple unlearning strategies, evaluation tooling, and certification workflows. Includes 41 unlearning strategies, 24 selectors, benchmark pipelines, documentation, and a test suite with 465 passing tests.",
      "url": "https://pypi.org/project/erasus/",
      "keywords": [
        "Python"
      ],
      "entity": "Machine unlearning framework"
    },
    {
      "name": "Pix Coreset",
      "description": "An interactive coreset generation tool for computer vision experiments, packaged with a FastAPI backend and Streamlit interface. Achieved 90% data compression for KNN segmentation workflows with Dockerized deployment, CI, and Pytest coverage.",
      "url": "https://github.com/OnePunchMonk/PixCoreset",
      "keywords": [
        "Python",
        "Computer Vision",
        "Docker",
        "FastAPI",
        "Pytest",
        "Streamlit"
      ],
      "entity": "Coreset proof of concept"
    },
    {
      "name": "Forget Me Net",
      "description": "A verifiable machine unlearning prototype built around SISA training, model serving, and downloadable proof artifacts. Reached 121-second shard-level unlearning and produced downloadable proof certificates backed by FastAPI, Streamlit, Transformers, and IPFS.",
      "url": "https://github.com/OnePunchMonk/ForgetMeNet",
      "keywords": [
        "Python",
        "FastAPI",
        "Streamlit",
        "Transformers",
        "IPFS"
      ],
      "entity": "Unlearning proof of concept"
    },
    {
      "name": "Coresets for Image Segmentation",
      "description": "A coreset-driven image segmentation project focused on reducing training data while preserving segmentation quality. Built as a focused project submission around coresets for image segmentation.",
      "url": "https://github.com/OnePunchMonk/Coresets-for-Image-Segmentation",
      "keywords": [
        "Python",
        "Machine Learning",
        "Computer Vision",
        "Segmentation"
      ],
      "entity": "Hackathon project"
    },
    {
      "name": "BTCTrade",
      "description": "A deep-learning trading bot that combines LSTM price forecasting with technical indicators including Bollinger Bands and moving averages. Validated with backtesting, reporting RMSE of 417, Sharpe ratio of 8.71, and Sortino ratio of 2.47.",
      "url": "https://github.com/OnePunchMonk/BTCTrade",
      "keywords": [
        "Python",
        "LSTM",
        "Backtesting",
        "Technical Indicators"
      ],
      "entity": "Hackathon project"
    }
  ],
  "skills": [
    {
      "name": "Languages",
      "keywords": [
        "Python",
        "SQL"
      ]
    },
    {
      "name": "ML and data",
      "keywords": [
        "PyTorch",
        "TensorFlow",
        "Benchmarking and evaluation"
      ]
    },
    {
      "name": "Backend and infra",
      "keywords": [
        "FastAPI",
        "PostgreSQL",
        "Docker",
        "Kafka",
        "REST APIs"
      ]
    },
    {
      "name": "Engineering practice",
      "keywords": [
        "Pytest",
        "Regression testing",
        "Research prototyping",
        "System design"
      ]
    }
  ]
}