{"$schema":"https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json","basics":{"name":"Loukik Naik","label":"Software Engineer","summary":"I’m a software engineer who enjoys building systems that ship and scale. I’ve worked across backend, systems, and ML-adjacent infrastructure, building APIs, real-time services, and production pipelines. I like owning problems end-to-end and keeping systems simple and reliable. I’m especially interested in: - Backend & systems engineering - ML / AI-powered products - Distributed systems & infrastructure MS in CS from UC San Diego. Always learning, building, and happy to connect.","location":{"city":"San Francisco, CA, United States"},"profiles":[{"network":"LinkedIn","username":"loukiknaik","url":"https://linkedin.com/in/loukiknaik"}]},"meta":{"canonical":"https://saywise.com/member4037","version":"v1.0.0","lastModified":"2026-10-06T02:57:12.279Z"},"x_saywise":{"handle":"member4037","pronouns":null,"availability":null,"oneLiner":"I’m a software engineer who enjoys building systems that ship and scale. I’ve worked across backend, systems, and ML-adjacent infrastructure, building APIs, real-time services, and production pipelines. I like owning problems end-to-end and keeping systems simple and reliable. I’m especially interested in: - Backend & systems engineering - ML / AI-powered products - Distributed systems & infrastructure MS in CS from UC San Diego. Always learning, building, and happy to connect.","profileUrl":"https://saywise.com/member4037","markdownUrl":"https://saywise.com/member4037/profile.md","pdfUrl":"https://saywise.com/member4037/resume.pdf","archetype":{"code":"HE-MX","name":"Junction Artisan","url":"https://saywise.com/sca/3625376f1e9e","rarity":"One of the first Junction Artisans on Saywise"}},"work":[{"name":"Eudia","position":"Software Engineer","location":"Palo Alto, California, United States","url":"https://eudia.com","startDate":"2025-08-01","endDate":"2026-03-01","summary":"• Owned the Mergers & Acquisitions backend service end-to-end, building and operating the core infrastructure for an agent-based AI system for large-scale legal document analysis in production. • Partnered closely with lawyers and product managers to design and ship product features, translating legal workflows and requirements into scalable platform capabilities. • Designed and shipped backend APIs serving as the control plane for AI agent execution, managing document ingestion, run orchestration, result persistence, and retrieval of structured and tabular outputs. • Re-architected agent orchestration by migrating from Airflow to Temporal, enabling reliable execution of long-running, stateful agents with retries, idempotency, and deterministic behavior. • Built backend capabilities for multiple AI agents, including flexible document analysis, configurable PII redaction, Contract Comparison, and tabular review with schema-based inference and cell-level operations, backed by structured logging, workflow tracing, telemetry, and Kubernetes-native monitoring. • Built workflows coordinating multiple services, queues, and workers, addressing production challenges including duplicate execution, inconsistent state across retries, and downstream dependency failures. • Designed and managed Postgres database schemas and migrations using Alembic, delivering backward-compatible schema changes to support new agent capabilities. • Converted the service to a multi-tenant architecture, enforcing tenant-aware routing, strict isolation, and safe rollouts across live customers. • Managed the rollout of new backend features across staging, internal, and production environments using Kubernetes, Helm, and ArgoCD, supporting multiple customer deployments."},{"name":"OpenFilter","position":"Contributor","location":"United States","url":"https://openfilter.io","startDate":"2025-05-01","endDate":"2025-08-01","summary":"OpenFilter is a universal abstraction for building and running computer vision workloads"},{"name":"Plainsight Technologies","position":"Machine Learning Engineer","location":"San Francisco, California, United States","url":"https://plainsight.ai","startDate":"2025-01-01","endDate":"2025-08-01","summary":"• Developed and deployed real-time image segmentation APIs using FastAPI for vision models including SAM and MobileSAM, enabling seamless integration into internal applications. • Containerized services with Docker and automated infrastructure deployment via Terraform and CI/CD pipelines, ensuring scalable, consistent rollouts. • Designed a model caching system using symlinks and config-driven scripts, reducing friction during builds and cutting cloud runtimes by avoiding redundant model downloads at runtime. • Created a Cron-triggered retraining pipeline that detects newly labeled data and launches Vertex AI training jobs, enabling continuous model updates. • Designed an OCR model evaluation framework utilizing edit distance, confidence scoring, and text similarity metrics to automate quality checks pre-deployment. • Designed and implemented a robust integration testing framework to validate end-to-end performance of the model generation pipelines for segmentation, object and keypoint detection tasks."},{"name":"Plainsight Technologies","position":"Machine Learning Intern - Computer Vision","location":"San Diego, California, United States","url":"https://plainsight.ai","startDate":"2024-06-01","endDate":"2024-12-01","summary":"• Implemented serverless functions on Google Cloud Functions for real-time video processing. • Developed and orchestrated Kubeflow pipelines on GKE for video preprocessing and filter evaluation, triggered by GCS data ingestion. • Created an environment calibration model for deriving environmental statistics from video streams. • Automated testing and deployment using GitHub Actions, PyPi, and Docker for production readiness. • Ensured reliability through end-to-end tests and continuous integration."},{"name":"UC San Diego","position":"Graduate Research Assistant","url":"https://ucsd.edu","startDate":"2024-05-01","endDate":"2024-06-01"},{"name":"Lab Systems (I) Pvt. Ltd.","position":"AI Developer","location":"Mumbai, Maharashtra, India","url":"https://labsystems.co.in","startDate":"2023-02-01","endDate":"2023-05-01","summary":"• Developed video forensics tool mvp for law enforcement agencies to analyse CCTV footage efficiently. • Researched different techniques of object and text recognition to improve performance of the software. • Leveraged Python libraries like pytesseract, easyocr, yolo, mediapipe, etc for object and text recognition"},{"name":"Hexaview Technologies Inc.","position":"Software Engineer","location":"Mumbai, Maharashtra, India","url":"https://hexaviewtech.com","startDate":"2022-07-01","endDate":"2022-08-01"}],"education":[{"institution":"UC San Diego","studyType":"Master of Science - MS in Computer Science","startDate":"2023-09-01","endDate":"2024-12-01"},{"institution":"University of Mumbai","studyType":"Bachelor's degree in Computer Engineering","startDate":"2019-10-01","endDate":"2023-05-01"},{"institution":"Pace Junior Science College","startDate":"2017-01-01","endDate":"2019-01-01"}],"projects":[{"name":"Verdict - Synthetic Market Research Agent","description":"- Built a synthetic focus-group engine that runs 200–2,000 Claude personas against a product, returns a 1 - 5 purchase-intent PMF via structured output, clusters qualitative themes, and drops an Opus 4.7 executive brief - FastAPI, Sonnet 4.6, SQLModel, React + TypeScript end-to-end. - Designed a Temporal-shaped async workflow with ContextVar-based token attribution, so concurrent simulations in one process correctly bill each Claude call back to the right sim_id without threading IDs through every callsite. - Replaced an embedder-based SSR scorer (which clustered everything into a 3.0–3.6 band regardless of sentiment) with an LLM scoring path: Sonnet 4.6 reads each multi-turn interview, returns the full intent distribution, and grounds personas in live market data via the web_search tool. - Shipped reusable persona libraries, A/B variant comparison with side-by-side KPI deltas, per-call cost/latency telemetry (~$1 per 10-profile sim), and a Vite + React UI auto-deployed to verdict.loukik.dev — inspired by the PyMC × Colgate 2025 LLMs Reproduce Purchase Intent paper."},{"name":"Podclipper - Long Podcasts to Reels in a Click","description":"• Built a local pipeline that turns long-form podcasts into vertical 9:16 reels — Whisper, Claude, YOLOv8, and OpenCV chained end-to-end. • Designed a stage-based architecture with content-hash-keyed caching, so iterating on crop or prompt logic runs in seconds instead of re-transcribing. • Solved active-speaker tracking across hard shot cuts via per-shot x-center clustering with look-ahead crop seeding and mouth-motion → diarized-turn linking. • Shipped an LLM-as-judge quality gate, a React + Vite landing page, and GitHub Actions auto-deploy to podclipper.loukik.dev."},{"name":"DraftIn - Keyboard-Driven LinkedIn Outreach Drafter","description":"- Built a local-first Chrome MV3 + Node pipeline that drafts personalized LinkedIn outreach from a screenshot buffer - Alt+K stacks viewports across LinkedIn profiles and JD pages, Alt+L ships them through Oracle (browser-driven ChatGPT, no API key, no SaaS), and the result lands on the clipboard via pbcopy. - Designed it screenshot-as-source-of-truth - no DOM parsing, no LLM tokens in the browser — and used activeTab + captureVisibleTab so the same buffer works on any page (career sites, founder pages, anywhere the user scrolls). - Solved Chrome MV3's clipboard-after-focus-loss limitation by writing through the local server with pbcopy / xclip / clip instead of fighting the service-worker clipboard restriction from a toolbar gesture. - Tuned Oracle browser-mode flags (--browser-min-stable-ms 15s, 10-min ceiling, 4-min recheck) so mid-stream pauses during image analysis and company web-search lookups don't trip premature capture; shipped a React + Vite landing page with GitHub Actions auto-deploy to draftin.loukik.dev."},{"name":"3D Vehicle Visualizer (Digital Twin + API)","description":"• Built a browser-based 3D vehicle viewer with a FastAPI backend that controls vehicle state (speed, brake lights, doors) and syncs it with a React + Three.js frontend. • Designed a digital-twin architecture where the backend owns state + configuration, keeping the UI purely as a renderer. • Solved real-world GLB inconsistencies via semantic part mapping, custom hinge/axis overrides, and per-model configuration stored via API. • Implemented production-style UX patterns: optimistic updates with rollback, debounced writes, and polling for multi-client consistency."},{"name":"Synapse – Decision-Based Spaced Repetition (iOS)","description":"• Built an offline-first iOS app that trains engineering intuition using scenario-based flashcards focused on tradeoffs instead of memorization. • Implemented a modified SM-2 spaced repetition engine with confidence-based scheduling and per-scenario difficulty tracking. • Designed a JSON-based import pipeline to convert LLM-generated content into structured study decks with validation and mapping to SwiftData models. • Architected clean separation between DTOs and persistence models to handle complex object graphs and ensure scalable content ingestion."},{"name":"AutoReel - Multi Agent Reel Generator","description":"• Built AutoReel, a side project that turns a single prompt into a ready-to-share 1-minute reel. • Set up a multi-agent pipeline (LangGraph): one agent writes the script, another generates images, another adds TTS audio, and the last stitches everything into video. • Used OpenAI models for script, voice, and visuals, and MoviePy for editing. • Designed the workflow to run end-to-end automatically, from idea → script → video — with no manual editing."},{"name":"Prompt Based Email Classifier","description":"• Built a smart Gmail organizer that uses a custom prompt via LangChain to classify emails through either Ollama (local models) or the Gemini API (cloud) for real-time classification into categories like Work or Finance giving users full control over privacy vs. accuracy. • Designed a fast, responsive system using multithreaded Python (Flask) and the Gmail API to fetch and batch-process emails, with a React frontend for live updates and intuitive filtering."},{"name":"Surfstore: A Distributed File Storage System","description":"• Built a Horizontally Scalable File Storage System in Go, storing data across multiple block and metadata servers. • This project makes use of a .proto file to generate grpc code so that the client and server stub can use different programming languages and can belong to different devices. • Implemented Raft consensus algorithm to sync changes across multiple metadata servers."},{"name":"PPG Signal Based Hypertension Prediction","description":"• Developed robust ETL pipeline to process the MIMIC-III dataset ensuring high data quality and integrity for downstream analytics. • Achieved a test accuracy of 74% and a sensitivity of 91% on the MIMIC-III dataset."},{"name":"TritonHTTP: A Server written in Go","description":"• Implemented a Go-based HTTP server from scratch to handle multiple virtual hosts, each serving content from distinct document roots. • Leveraged Go's concurrency model to handle multiple client connections simultaneously using goroutines. • The server parses the incoming requests and, based on the specifications of TritonHTTP, generates and serves 200, 400, and 404 responses."},{"name":"Active Amigo - Exercise Buddy App","description":"• Our team developed an Android app that helps users find others with similar schedules for working out or participating in physical activities. • The app uses Firebase Firestore as the backend and includes an end-to-end CI/CD setup with GitHub Actions."},{"name":"Performance Analysis of Co-ordinate Descent vs Logistic Regression","description":"• In this analysis study I performed a brief comparison of the performance of Logistic regression and 3 different varieties of co-ordinate descent i.e. Random Co-ordinate Descent, Iterative Co-ordinate Descent and Gradient based Co-ordinate Descent on the wine data set from sk-learn. • Although, the co-ordinate descent failed to achieve log-loss values comparable to logistic regression, they converged faster and had reasonable loss and accuracy values to be considered for practical use."},{"name":"Prototype Selection Technique For 1-Nearest Neighbour","description":"• Compared and proposed different methods for prototype selection on huge datasets to summarize the data such that 1-NN model yields optimal test results. • Achieved identical test accuracy of 96.7% using only about 11% of MNIST dataset after prototyping."},{"name":"Anime Recommendation Website","description":"Developed a website that suggests similar animes to a given anime entered by the user. Implemented a cosine similarity algorithm to calculate the similarity between anime vectors."}],"skills":[{"name":"Agentic Workflows"},{"name":"STT"},{"name":"Whisper"},{"name":"FastAPI"},{"name":"Swift (Programming Language)"},{"name":"iOS Development"},{"name":"Mobile Application Development"},{"name":"Xcode"},{"name":"3D Modeling"},{"name":"Video Processing"},{"name":"Software Infrastructure"},{"name":"Multi-agent Systems"},{"name":"Airflow"},{"name":"Temporal"},{"name":"LangGraph"},{"name":"TTS"},{"name":"Text-to-Image Generation"},{"name":"LangChain"},{"name":"LLaMA"},{"name":"Flask"},{"name":"Prompt Engineering"},{"name":"Multithreading"},{"name":"Routing Protocols"},{"name":"Large Language Models (LLM)"},{"name":"Google Kubernetes Engine (GKE)"},{"name":"Cloud Infrastructure"},{"name":"Kubeflow"},{"name":"Google Cloud Platform (GCP)"},{"name":"Docker"},{"name":"Computer Vision"},{"name":"Machine Learning"},{"name":"Data Visualization"},{"name":"Remote Procedure Call (RPC)"},{"name":"Concurrent Programming"},{"name":"Computer Networking"},{"name":"Go"},{"name":"Data Synchronization"},{"name":"HTTP"},{"name":"Problem Solving"},{"name":"Programming"},{"name":"Computer Science"},{"name":"Data Structures"},{"name":"Algorithms"},{"name":"Natural Language Processing (NLP)"},{"name":"Software Project Management"},{"name":"Data Preparation"},{"name":"Data Analytics"},{"name":"Data Science"},{"name":"Statistics"},{"name":"Model Evaluation"}],"certificates":[{"name":"SnowConvert for Developers","date":"2026-01-01","issuer":"Snowflake"},{"name":"Generative AI: Working with Large Language Models","date":"2025-09-01","issuer":"LinkedIn"},{"name":"Develop a Synthetic Monitoring Platform","date":"2024-09-01","issuer":"Build Fellowship by Open Avenues"},{"name":"Software Engineer Intern","date":"2024-03-01","issuer":"HackerRank"},{"name":"Neural Networks and Deep Learning","date":"2022-01-01","issuer":"Coursera"},{"name":"SQL(Intermediate)","date":"2022-01-01","issuer":"HackerRank"},{"name":"Codechef Snackdown","date":"2021-12-01","issuer":"CodeChef"},{"name":"18th Rank | Code Wars","date":"2021-03-01","issuer":"Coding Ninjas"},{"name":"Data Structures in Python","date":"2020-06-01","issuer":"Coursera"}]}