resume.json

{
  "basics": {
    "name": "Krishna Penukonda",
    "label": "Full Stack Software Engineer",
    "image": "https://avatars.githubusercontent.com/u/13855549?v=4",
    "email": "[email protected]",
    "website": "https://penukonda.me",
    "summary": "Building scalable agentic systems for enterprise ecommerce workflows at Hypotenuse AI, owning the engineering pipeline end-to-end.\n\nCore strengths include Full Stack Development, Generative AI, LLMOps, and Site Reliability Engineering.\n\nPreviously developed computer vision models for instance segmentation and real-time object detection systems at Qritive and Red Dot Robotics.",
    "location": {
      "city": "Singapore",
      "countryCode": "SG"
    },
    "profiles": [
      {
        "network": "LinkedIn",
        "username": "krishna-penukonda",
        "url": "https://www.linkedin.com/in/krishna-penukonda"
      },
      {
        "network": "GitHub",
        "username": "tasercake",
        "url": "https://github.com/tasercake"
      }
    ],
    "url": "https://penukonda.me"
  },
  "work": [
    {
      "name": "Hypotenuse AI (YC Summer '20)",
      "position": "Lead Software Engineer",
      "location": "Singapore",
      "url": "https://hypotenuse.ai",
      "startDate": "2021-01-04",
      "summary": "First full-time hire, owned engineering end-to-end at Hypotenuse AI. Architecting and scaling AI-powered content and image pipelines, cloud infrastructure, and DevOps workflows while mentoring the team and working with product to drive reliable, high-impact releases.",
      "highlights": [
        "Led architecture and scaling of AI content generation systems to 1M+ users with P99 latency <500ms and >99.999% availability, owning reliability, cost, and roadmap trade-offs.",
        "Mentored and onboarded 25+ engineers; set coding/ops guidelines and paired on designs used by multiple product lines.",
        "Drove team-wide developer velocity: -70% CI/test runtime and -50% pull request turnaround time via pipeline redesign, parallelism, caching, and review automation.",
        "Built resilient async task scheduling for agentic AI workflows, ensuring fault-tolerant retries, idempotency, prioritization, and throughput under enterprise loads",
        "Cut AWS compute cost ~30% through workload profiling, S3 offload, instance mix, and autoscaling policy updates",
        "Built and productized LLM-backed features (RAG with <2s retrieval, real-time guardrails), improving factuality and boosting user trust",
        "Implemented LLM guardrails and feedback loops with fine-tuning pipelines (Python/SQL/Jupyter), improving factual accuracy and safety of generated content.",
        "Designed LLM prompt-chaining framework for translation, structured data extraction, and brand-voice copywriting, enabling consistent, scalable outputs across customer use-cases.",
        "Shipped image-generation service to 10K+ MAUs; cut P99 latency from 60 s to <20 seconds",
        "Drove full-stack architecture and implementation across React/TypeScript front-end, FastAPI/Python services, and Rust performance modules",
        "Implemented Redis GCRA / sliding-window rate limiting system that curbed abuse and improved conversion metrics.",
        "Instituted unified observability (metrics/tracing/logs) via OpenTelemetry, Grafana, SigNoz that reduced MTTA <60s and MTTR <1h; led incident reviews and set reliability standards across the team.",
        "Built one-click CI/CD (GitHub Actions → Elastic Beanstalk/ECS) with sub-10-min builds and deployments.",
        "Owned SaaS platform foundations (billing, access control, anti-abuse systems) ensuring security, scalability, and compliance for enterprise customers.",
        "Designed polyglot persistence for product search (OLTP + ElasticSearch) to keep <10ms read paths while enabling complex queries at scale"
      ],
      "description": ""
    },
    {
      "name": "Qritive",
      "position": "Computer Vision Engineer",
      "url": "https://qritive.com",
      "startDate": "2018-10",
      "endDate": "2018-12",
      "summary": "Developed Object Detection and Instance Segmentation models for use on gigapixel-scale medical images",
      "location": "Singapore",
      "highlights": [
        "Technologies: Python, Keras, TensorFlow, OpenCV, OpenSlide"
      ]
    },
    {
      "name": "Red Dot Robotics",
      "position": "Computer Vision Engineer",
      "startDate": "2018-05",
      "endDate": "2018-09",
      "summary": "Developed real-time Object Detection and Tracking models for deployment on autonomous vehicles",
      "location": "Singapore",
      "highlights": [
        "Technologies: Python, Keras, TensorFlow, OpenCV"
      ]
    }
  ],
  "education": [
    {
      "institution": "Singapore University of Technology and Design (SUTD)",
      "area": "Information Systems Technology and Design",
      "studyType": "Bachelor of Engineering",
      "endDate": "2020-09"
    }
  ],
  "skills": [
    {
      "name": "Core Skills",
      "keywords": [
        "Generative AI",
        "LLMOps",
        "Full Stack Development",
        "Site Reliability Engineering",
        "Prompt engineering",
        "Technical Leadership",
        "DevOps",
        "Cloud Architecture"
      ]
    },
    {
      "name": "Technologies",
      "keywords": [
        "Python",
        "TypeScript",
        "React.js",
        "FastAPI",
        "PostgreSQL",
        "Celery",
        "DynamoDB",
        "Redis",
        "AWS",
        "OpenTelemetry",
        "GitHub Actions",
        "Vector Databases",
        "Git",
        "Docker",
        "PyTorch",
        "Next.js",
        "Rust"
      ]
    }
  ]
}