{
  "$schema": "https://raw.githubusercontent.com/jsonresume/jsonresume.org/master/packages/schema/schema.json",
  "meta": {
    "canonical": "https://rsnair.tech/resume.json",
    "version": "1.0.0",
    "lastModified": "2026-08-11T00:00:00-04:00",
    "source": {
      "filename": "nair_rohit_resume_2026-1.pdf",
      "sha256": "bc4ce7b1022df89e0075bfcec101157758fa088fe936e86d20b938f9074b6a1b",
      "extractedAt": "2026-08-11"
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    "privacy": "The public JSON omits the phone number present in the source PDF."
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  "basics": {
    "name": "Rohit Nair",
    "label": "AI Engineer",
    "image": "https://rsnair.tech/static/avi.png",
    "email": "rsnair@umich.edu",
    "url": "https://rsnair.tech/",
    "summary": "AI Engineer shipping LLM workflows and agentic toolkits that augment software engineering. Focused on multi-agent orchestration, hybrid RAG, distributed inference, and developer-in-the-loop automation.",
    "location": {
      "city": "Detroit",
      "region": "Michigan",
      "countryCode": "US"
    },
    "profiles": [
      {
        "network": "GitHub",
        "username": "rnair98",
        "url": "https://github.com/rnair98"
      },
      {
        "network": "LinkedIn",
        "username": "r-s-nair",
        "url": "https://www.linkedin.com/in/r-s-nair/"
      }
    ]
  },
  "work": [
    {
      "name": "Ford Motor Company",
      "location": "Dearborn, Michigan (hybrid)",
      "position": "AI/ML Software Engineer",
      "startDate": "2023-07",
      "summary": "Artificial Intelligence Advancement Center. Python, Java, PyTorch, LangGraph, Ray, vLLM, Airflow, FastAPI, Docker, GCP Cloud Run, Kubernetes/OpenShift, Terraform, Arize Phoenix, and OpenTelemetry.",
      "highlights": [
        "Owned architecture and infrastructure for a LangGraph multi-agent pull-request review GitHub App, scaling to more than 1,000 product teams and 800–850 pull requests per day.",
        "Built diff-aware context and retrieval over repository and pull-request artifacts with multi-hop reasoning to detect actionable anti-patterns beyond CI linters; achieved 70% precision, 56.5% recall, and 59.4 F1 per pull request.",
        "Led platform hardening across Cloud Run services and tooling, human-in-the-loop escalation, and an in-house evaluation harness with guardrails and tracing through Arize Phoenix and OpenTelemetry."
      ]
    },
    {
      "name": "Ancor Automotive",
      "location": "Troy, Michigan",
      "position": "Software Engineer",
      "startDate": "2023-04",
      "endDate": "2023-07",
      "summary": "C#, ASP.NET MVC, T-SQL, TypeScript, jQuery, and Bootstrap.",
      "highlights": [
        "Led an EV battery Track & Trace platform with a tamper-evident ledger to improve supply-chain transparency and auditability.",
        "Drove requirements with more than five internal and external stakeholders and translated them into specifications and UI workflows, contributing to a 25% increase in EV client satisfaction scores."
      ]
    },
    {
      "name": "Ford Motor Company",
      "location": "Allen Park, Michigan",
      "position": "Software Engineer Intern",
      "startDate": "2019-05",
      "endDate": "2019-08",
      "summary": "Ford Customer Service Division. Java, Spring Boot, Angular, TypeScript, and PostgreSQL.",
      "highlights": [
        "Helped refactor a monolithic vehicle-diagnostics application into domain-aligned services using domain-driven design patterns and delivered API and frontend work in an Agile team."
      ]
    }
  ],
  "education": [
    {
      "institution": "University of Michigan",
      "area": "Computer Science & Data Science",
      "studyType": "Bachelor of Science",
      "startDate": "2020-12",
      "endDate": "2023-08",
      "courses": [],
      "summary": "Societies: Upsilon Pi Epsilon; Michigan Student Artificial Intelligence Lab."
    }
  ],
  "certificates": [
    {
      "name": "AI Evals for Engineers & PMs"
    },
    {
      "name": "Mastering LLMs for Developers & Data Scientists"
    }
  ],
  "skills": [
    {
      "name": "AI/ML",
      "keywords": [
        "PyTorch",
        "TensorFlow",
        "Hugging Face",
        "LangChain",
        "LangGraph",
        "vLLM",
        "Ray",
        "Airflow"
      ]
    },
    {
      "name": "Backend and cloud",
      "keywords": [
        "FastAPI",
        "Spring Boot",
        "Docker",
        "Kubernetes",
        "OpenShift",
        "Terraform",
        "GCP",
        "Azure"
      ]
    },
    {
      "name": "Data and tooling",
      "keywords": [
        "SQL",
        "Spark",
        "BigQuery",
        "Arize Phoenix",
        "OpenTelemetry",
        "Git"
      ]
    }
  ],
  "projects": [
    {
      "name": "Antisomnus",
      "description": "A real-time driver-fatigue detection pipeline using face landmarks and CNN embeddings, deployed on GCP with a Flutter client.",
      "highlights": [
        "Built with Python, OpenCV, MediaPipe, TensorFlow/Keras, EfficientNetV2, GCP, Flutter, and Firebase."
      ],
      "url": "https://github.com/The-Rockers/DriverDrowsinessDetection",
      "keywords": [
        "computer vision",
        "driver drowsiness detection",
        "machine learning"
      ]
    }
  ]
}