resume.json

{
  "basics": {
    "name": "Peeush Agarwal",
    "label": "Senior ML Engineer",
    "email": "agarwal.peeush@gmail.com",
    "phone": "+91 82377 28795",
    "location": {
      "city": "Pune",
      "region": "Maharashtra",
      "countryCode": "IN"
    },
    "profiles": [
      {
        "network": "LinkedIn",
        "username": "peeushagarwal",
        "url": "https://www.linkedin.com/in/peeushagarwal"
      }
    ],
    "summary": "AI-first engineer with 14+ years of building and shipping production ML and software systems. I architect, deploy, and scale solutions. From LLMs and NLP pipelines to computer vision systems, I've led end-to-end AI solutions that cut deployment time by 50% and inference time by 40%. Currently leading AI/ML initiatives at The Mosaic Company, I thrive at the intersection of fast execution and deep technical ownership. Always reading. Always building."
  },
  "skills": [
    {
      "name": "Generative AI",
      "level": "Advanced",
      "keywords": [
        "AzureOpenAI",
        "Microsoft agent framework",
        "LangChain",
        "LlamaIndex",
        "RAG / Vector DBs",
        "Agentic AI"
      ]
    },
    {
      "name": "Machine Learning",
      "level": "Advanced",
      "keywords": [
        "Deep Learning",
        "NLP",
        "Computer Vision",
        "MLOps",
        "Model Optimization",
        "Feature Engineering",
        "Transfer Learning"
      ]
    },
    {
      "name": "Cloud & MLOps",
      "level": "Proficient",
      "keywords": [
        "AzureML",
        "AWS",
        "GCP",
        "Docker",
        "Kubernetes",
        "CI/CD",
        "Monitoring"
      ]
    },
    {
      "name": "Technologies/Tools",
      "level": "Proficient",
      "keywords": [
        "PyTorch",
        "TensorFlow",
        "Kafka",
        "Django",
        "Flask",
        "Jenkins",
        "Git"
      ]
    },
    {
      "name": "Programming languages",
      "level": "Proficient",
      "keywords": ["Python", "Golang", "C#", "SQL"]
    },
    {
      "name": "Leadership & Architecture",
      "level": "Advanced",
      "keywords": [
        "Technical Leadership",
        "Software Architecture",
        "Distributed Systems",
        "Cross-functional Collaboration",
        "Mentoring & Coaching",
        "Project Roadmaps"
      ]
    }
  ],
  "work": [
    {
      "name": "The Mosaic Company",
      "company": "The Mosaic Company",
      "position": "Senior ML Engineer",
      "startDate": "2025-01",
      "location": "Pune, IN",
      "summary": "Leading agentic AI initiatives and a cross-functional team of 4 to design, deploy, and scale LLM-based agents and multi-agent orchestration that automate operational workflows. Responsible for agent architecture, production deployment, monitoring, safety guardrails, and human-in-the-loop controls.",
      "highlights": [
        "Led agentic AI initiatives: designed LLM-based agents that assist Finance Team with the automated summary of important commodity reports, reducing their manual effort by 30% and improving decision-making speed.",
        "Architected multi-agent orchestration workflows for planning and execution, enabling autonomous end-to-end task completion and reducing manual intervention.",
        "Implemented tool-augmented LLM agents with retrieval (vector DBs), tool executors, and RAG strategies using LlamaIndex and AzureOpenAI.",
        "Owned production deployment, monitoring, and safety guardrails for agentic systems, including logging, fallback policies, and human-in-the-loop controls.",
        "Mentored the cross-functional team on agent design patterns, experiment-driven development, and KPIs for agent-driven automation."
      ]
    },
    {
      "name": "Process Point Technologies (PPT)",
      "company": "Process Point Technologies (PPT)",
      "position": "Senior ML Engineer",
      "startDate": "2022-08",
      "endDate": "2025-01",
      "location": "Pune, IN",
      "summary": "PPT is a technical consultant company with expertise in AI, ML, DL, ERP domain. Led engineering initiatives for model deployment and infrastructure.",
      "highlights": [
        "Developed and deployed a Golang-based Traffic Simulator API reducing inference time by 40%.",
        "Engineered and integrated MLOps pipeline in AzureML, cutting deployment time by 50%.",
        "Implemented MLOps pipelines for the Mine Vision System, integrating Computer Vision models into AzureML.",
        "Designed and implemented a PowerBI dashboard for microservices, providing comprehensive insights.",
        "Collaborated with cross-functional teams to deliver production-grade ML solutions."
      ]
    },
    {
      "name": "Privacera (Freestone Infotech Pvt. Ltd.)",
      "company": "Privacera (Freestone Infotech Pvt. Ltd.)",
      "position": "Senior ML Engineer",
      "startDate": "2021-11",
      "endDate": "2022-08",
      "summary": "Privacera is into the data governance domain, based in US.",
      "highlights": [
        "Utilized NER based spaCy model to identify entities within textual data and subsequently masked these entities which increased the performance by 35%",
        "Focused on utilizing a Medical NER model for entity recognition within the medical textual data to improve accuracy by 20%",
        "Constructed a feedback-driven pipeline for reassessing model-generated entities based on user feedback"
      ]
    },
    {
      "name": "EuroForm A/S",
      "company": "EuroForm A/S",
      "position": "Senior Software Developer",
      "startDate": "2013-05",
      "endDate": "2021-11",
      "summary": "EuroForm provides Managed Print Services oriented solutions to its client-base worldwide. Led end-to-end ML model lifecycle and collaborated across teams.",
      "highlights": [
        "Supervised end-to-end ML model lifecycle from data collection to deployment and monitoring.",
        "Led integration of ML models into cloud-based APIs and production services.",
        "Solved highly imbalanced datasets using clustering and targeted feature engineering."
      ]
    },
    {
      "name": "Hewlett Packard (HP)",
      "company": "Hewlett Packard (HP)",
      "position": "Staff Software Engineer",
      "startDate": "2011-07",
      "endDate": "2013-05",
      "summary": "Hewlett Packard (HP) is a global technology company. Built REST-based web service and integrated with internal systems to provide data for printer fleet management.",
      "highlights": [
        "Designed and implemented a REST-based web service for printer fleet management.",
        "Integrated the web service with internal systems to provide real-time data for monitoring and analytics.",
        "Collaborated with cross-functional teams to ensure seamless integration and deployment."
      ]
    }
  ],
  "education": [
    {
      "institution": "IIIT Bangalore & upGrad",
      "area": "Data Science",
      "studyType": "Post Graduate Diploma in Data Science",
      "startDate": "2017-12",
      "endDate": "2018-12",
      "location": "Bengaluru, IN"
    },
    {
      "institution": "National Institute of Technology, Durgapur",
      "area": "Computer Science",
      "studyType": "B. Tech.",
      "startDate": "2007-07",
      "endDate": "2011-05",
      "location": "Durgapur, WB, IN",
      "gpa": "7.93"
    }
  ],
  "languages": [
    {
      "language": "Python",
      "fluency": "Advanced"
    },
    {
      "language": "Golang",
      "fluency": "Intermediate"
    }
  ],
  "interests": [
    {
      "name": "Currently exploring",
      "keywords": [
        "LLM agents for logistics automation",
        "RAG pipelines",
        "Real-time inference optimization"
      ]
    }
  ],
  "projects": [
    {
      "name": "Mine Vision System (MLOps + CV)",
      "summary": "Problem: Deploying CV models reliably in mining environments. Approach: Built end-to-end pipeline using AzureML with CI/CD, monitoring, and feedback loops. Outcome: Productionized CV models with streamlined retraining and monitoring."
    },
    {
      "name": "Traffic Simulator API (Golang + ML)",
      "summary": "Problem: Slow inference for real-time traffic simulation. Approach: Implemented a Golang-based API and optimized model serving. Outcome: Reduced inference time by ~40% in production."
    },
    {
      "name": "Medical NER Pipeline (spaCy)",
      "summary": "Problem: Extracting and masking sensitive medical entities. Approach: Built spaCy-based NER with feedback-driven retraining. Outcome: Improved entity recognition accuracy by ~20% and enabled safe data handling."
    },
    {
      "name": "Cross-domain ML Delivery (Mosaic)",
      "summary": "Problem: Fast delivery of tabular, CV, and NLP models to production. Approach: Led architecture and MLOps practices across domains. Outcome: Reduced time-to-production by 30% and standardized deployment patterns."
    }
  ],
  "volunteer": [],
  "publications": [],
  "awards": [],
  "references": [],
  "meta": {
    "theme": "consultant-polished"
  }
}