At the top of the page

Open to full stack and AI engineering roles

I build things that run on the web.

SWE | AI Engineer building scalable backends and AI-powered products end to end.

jayesh — zsh

Prefer the short version? The CV covers education, dates and the full project list on one page.

/about

Building AI systems that work beyond the demo.

I build production-grade AI and backend systems, along with the interfaces that sit on top of them — from APIs and RAG pipelines to polished, real-world products.

I’ve completed my B.Tech in Computer Science and gained experience through ML, and UI/UX internships, along with remote work for clients across different projects.

Interests

  • Full stack development
  • AI & LLM applications
  • Backend architecture
  • System design
View Full Resume

Experience

  • AI/ML Engineer Intern

    CrystalTech Solutions

    Built and optimised ML models across data preprocessing, training, and deployment for real-world applications.

  • Project Intern

    Smart City India Pvt. Ltd.

    Contributed to ITMS and road-safety systems, working on smart traffic management and urban infrastructure tooling.

  • Freelance UI/UX Designer

    Independent

    Designed interfaces for web and mobile clients, focused on usability and visual clarity rather than decoration.

/stack

The stack I work across.

Four layers, each with one job. Every tool here is one I have actually shipped with.

  • Interface

    What the user touches.

    • ReactReactUI components
    • Next.jsNext.jsRouting, SSG
    • TypeScriptTypeScriptTypes end to end
    • Tailwind CSSTailwindUtility styling
    • FramerFramerAnimation
  • API

    Contracts, validation, error paths.

    • PythonPythonServices, scripts
    • FastAPIFastAPIAsync APIs
    • Node.jsNode.jsRuntime
    • ExpressExpressREST routing
    • LangChainLangChainLLM pipelines
    • GroqFast inference
    • OllamaOllamaLocal models
  • Data

    Schemas, queries, embeddings.

    • PostgreSQLPostgreSQLRelational data
    • SupabaseSupabaseAuth + Postgres
    • MongoDBMongoDBDocument store
    • FirebaseFirebaseRealtime, auth
    • Hugging FaceHugging FaceEmbeddings
    • RedisRedisCache, queues
  • Systems

    Deploys, containers, versions.

    • VercelVercelEdge deploy
    • RenderRenderService hosting
    • DockerDockerContainers
    • GitGitVersion control
    • GitHubGitHubRepos, CI

/projects

Things I've Built

Real projects, each one built end to end — interface, API, and the data underneath.

  • AgentDock interface

    AgentDock

    A terminal-native AI agent with multiple interaction modes

    A CLI-first AI agent inspired by tools like ClawCode, built to handle tasks directly from the terminal while supporting different ways of working. Agent mode executes tasks, Plan mode structures the work first, and Ask mode keeps interactions focused on questions and explanations.

    • Terminal-native AI agent interface
    • Agent, Plan, and Ask interaction modes
    • Task execution through the CLI
    • Telegram interface for remote agent access
    • Switch between interaction modes based on the task
    • Bun
    • TypeScript
    • Commander.js
    • +2
  • PadhAI interface

    PadhAI

    A production-ready school ERP for managing academics, classrooms, and students

    A full-stack school management platform built for real-world deployment, connecting administrators, teachers, and students through role-based workflows — from school and class management to digital classrooms and teacher-created learning modules.

    • Role-based admin, teacher, and student access
    • School, class, section, subject, and classroom management
    • Teacher-created modules and learning content
    • Student access to classes and teacher content
    • Administrative controls for managing the school
    • Next.js
    • TypeScript
    • Tailwind CSS
    • +5
  • Birju.AI interface

    Birju.AI

    AI career and learning assistant

    Generates a staged learning roadmap for a chosen career path, then produces the quizzes and counselling report for each stage. Built around a sequence rather than a chat box, so a learner always knows what comes next.

    • Stage-wise roadmap generated per career path
    • Quizzes and reports produced per completed stage
    • Next.js frontend over a LangChain backend
    • Progress carried across sessions
    • Next.js
    • LangChain
    • Python
    • +1
  • DocuMind interface

    DocuMind

    AI-powered Q&A over your own PDFs

    A RAG-based document Q&A system that lets users upload PDFs and ask questions grounded in their content, with relevant source passages returned alongside each answer.

    • PDF upload and document chunking
    • Embedding-based semantic retrieval
    • Grounded answers using Groq-hosted LLMs
    • Source passages shown with responses
    • Python
    • FastAPI
    • LangChain
    • +3

/contact

Let's work together

Have an opportunity or project in mind? Send a quick message — I usually respond within 24 hours.

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