Swastik Mukherjee - AI Engineer & Full Stack Developer

    Swastik
    Mukherjee

    AI Engineer & Full‑Stack Developer

    Shipping AI‑powered web apps, multi‑agent systems, and ML pipelines. I build end-to-end—from training models to deploying production backends.

    Portfolio
    Chennai, IN·
    Scroll
    © 2026

    I’m Swastik Mukherjee, a Full Stack and AI developer who builds intelligent, real-world applications at the intersection of software engineering and machine learning. I specialize in creating scalable products that move from idea to deployment.

    10+

    Projects

    3

    Year

    Learning

    Selected Work

    Projects

    Each built from scratch, shipped to real users, and maintained in production.

    4Projects
    Featured
    01
    Project 014 total

    HelixAI

    Local-first multi-agent orchestration system

    Desktop AI orchestrator that runs complex workflows without cloud dependency.

    • Decomposes user requests into DAG task workflows, routes to specialized workers via Redis Streams
    • Handles LLM inference (Ollama), tool execution, RAG lookups, and streaming outputs locally
    • FastAPI orchestrator + PostgreSQL state + Qdrant vector search, all running on your machine
    Tech
    ElectronReactTypeScriptFastAPIPythonRedis StreamsPostgreSQLQdrantOllama
    02
    Project 024 total

    Real-Time Fraud Detection

    Streaming fraud scoring pipeline with low-latency inference

    Scores transactions in real-time using XGBoost converted to ONNX.

    • XGBoost model converted to ONNX for fast CPU inference in production
    • Spring Boot backend serves predictions with feature preprocessing pipeline
    • Next.js dashboard for monitoring fraud patterns and model performance
    Tech
    JavaSpring BootXGBoostONNXONNX RuntimeTypeScriptNext.js
    03
    Project 034 total

    FaceCheck.AI

    CNN classifier for real vs. AI-generated faces

    Detects synthetic faces using a custom CNN trained on a large dataset.

    • Custom CNN architecture trained on real vs. AI-generated face dataset
    • FastAPI backend serves TensorFlow/Keras inference with confidence breakdowns
    • Clear API boundaries between frontend and ML backend, containerized with Docker
    Tech
    Next.jsFastAPITensorFlowKerasDockerTypeScript
    04
    Project 044 total

    MindMend

    AI-backed journaling and mental wellness platform

    Conversational journaling app with structured AI responses and voice interactions.

    • Gemini-powered conversations with ElevenLabs voice synthesis for accessibility
    • Progress tracking, mood journaling, and reflective dialogue features
    • Firebase Auth + MongoDB Atlas backend, deployed on Vercel
    Tech
    Next.jsReactTypeScriptExpressNode.jsFirebase AuthMongoDB AtlasGemini APIElevenLabs

    I'm best at wiring up AI models to real products—from data to model to UI.

    Technical Stack

    Languages

    Core programming languages I use daily in production.

    Core
    TypeScriptPythonJavaScriptCC++
    Working Knowledge
    SQLGoJavaRustBash

    Full-Stack

    Frameworks and tools I reach for when building web apps.

    Core
    Next.jsReactNode.jsExpressFastAPIMongoDB
    Working Knowledge
    PostgreSQLTailwind CSSElectronFramer MotionZustandFlutter

    AI / ML

    Core frameworks and libraries I use for building, training, and deploying ML systems and workflows.

    Core
    PyTorchTensorFlowScikit-learnLangChain
    Working Knowledge
    TransformersKerasXGBoostRAG frameworksOllamaQdrantONNXSentence-TransformersHugging Face

    Tools & Infrastructure

    Dev tooling and infrastructure I rely on for building routes to production.

    Core
    DockerGitHubGitVS CodeLinuxVercel
    Working Knowledge
    RedisSupabaseFirebaseGitHub ActionsRenderGrafanaPrometheusPostman

    Contact

    Looking for an AI or full‑stack intern?

    2nd-year B.Tech student. I build real systems, ship working code, and own features end-to-end. Available for internships.

    Also on