Freelance Software Engineers in MaharashtraFreelance Software Engineers in Maharashtra
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Kamlesh Pokiya's avatar
Kamlesh Pokiya
Mumbai, India
Full Stack Developer | React.js, Node.js, Laravel, SaaS, AI
Full Stack Developer | React.js, Node.js, Laravel, SaaS, AI
Cover image for Delivered an end-to-end AI platform for a client—chat system...
Delivered an end-to-end AI platform for a client—chat system, dashboard, code features, smooth UX. If you need a similar setup, I’m available.
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Cover image for Proud to Announce My Latest Full-Stack Build: Flowyze! I rec...
Proud to Announce My Latest Full-Stack Build: Flowyze! I recently developed Flowyze — a powerful, end-to-end platform built using modern full-stack technologies. From scalable architecture to smooth UI/UX, every part of this project was crafted to handle real business workflows and deliver a premium experience. 💡 Tech Highlights: ✔ Full-stack architecture (frontend + backend + APIs) ✔ High-performance, scalable system ✔ Secure, clean, and modular codebase ✔ Optimized for real users & business operations If you’re looking to build a SaaS, dashboard, automation tool, or custom web platform, I’d love to help bring your idea to life with the same precision and quality. 💬 Open for new projects — let’s build something powerful!
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Cover image for 🚀 Just shipped a new MERN project! Proud to share TalentsLi...
🚀 Just shipped a new MERN project! Proud to share TalentsList.com (http://TalentsList.com), a modern talent-matching platform I developed using the MERN stack (MongoDB, Express, React, Node). Fast, scalable, and built with clean UI + solid backend architecture. If you're looking to build or revamp your web app, SaaS product, or custom platform, I’d love to help bring your idea to life. Open to new projects — Let’s build something great together!
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Cover image for Over the past few months, I’ve been helping teams streamline...
Over the past few months, I’ve been helping teams streamline and scale their operations using n8n — and the results have been amazing. From cutting repetitive tasks to building full logic-driven systems, n8n has become my go-to tool for smart, scalable automation. 🔧 What I Build with n8n 1️⃣ Multi-step, logic-heavy automations 2️⃣ Deep API integrations (REST, OAuth2, custom logic) 3️⃣ Lead capture → CRM workflows 4️⃣ Stripe, Notion, Slack, Airtable & more 5️⃣ Error-handled, production-grade processes 6️⃣ Real-time alerts & webhook-driven flows 💡 Why n8n? - Open-source, flexible, and highly customizable - Scales without pricey SaaS tools - Great for businesses wanting automation built their way - Integrates with almost anything If you’re looking to automate or upgrade your workflows, I’d be happy to help.
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Software Engineer(4)
Aniket Ghavte's avatar
Aniket Ghavte
Pune, India
AI-Native Full Stack Engineer
New to Contra
AI-Native Full Stack Engineer
Cover image for I shipped an open-source package
I shipped an open-source package called Fold. Here's the problem it solves: When you build an AI agent that touches more than one data source S3, Slack, a local database, your filesystem you end up managing three or four different SDKs, auth flows, and API shapes before writing a single line of actual agent logic. Then you give your LLM a list of 12 tools and hope it picks the right one. Fold takes a different approach. It's a virtual filesystem. You mount any backend under a path prefix: /notes → your local Documents folder /db → a SQLite database /s3 → an S3 bucket /slack → Slack channels as directories /model → a locally running Ollama model Your agent gets one tool: bash. cat, ls, grep, wc, head, pipe across all of them. LLMs were trained on bash. They know it cold. Fold exploits that instead of fighting it. Three things I built that Mirage (the main prior art) doesn't have: Local-first. SQLite, local files, and Ollama are first-class resources. Everything can run on-device with zero data egress and zero API keys. Reactive. ws.watch (http://ws.watch)() lets any resource push events when data changes. Your agent triggers automatically no polling, no cron. Rich listings. ls -c returns metadata alongside filenames row counts, schemas, member counts, last activity. The agent understands the environment in one call instead of five. It also ships as an MCP server out of the box. One file and it's available in Claude, Cursor, or any MCP client. Published under The Builder Company: npm install @tbc-fold/node Repo: https://lnkd.in/g6EdaYEx (https://lnkd.in/g6EdaYEx)Website: https://lnkd.in/g6WjxGGd Built this alongside Aura. The two are related Fold is the abstraction that lets Aura's local agent work across your data without sending anything to the cloud. If you're building agents and hitting the multi-SDK problem, try it.
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Cover image for DQN Web3 Growth Engine
DQN Web3 Growth Engine
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Cover image for Bango Payments
Bango Payments
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Cover image for AURA an AI native browser
AURA an AI native browser - which thinks and becomes your second brain fr..
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Software Engineer(5)
Ariston IT Services's avatar
Ariston IT Services
Pune, India
Ariston IT: Elevate Your Business with Custom Software
Ariston IT: Elevate Your Business with Custom Software
Cover image for Tour & Guide Booking and Management
Tour & Guide Booking and Management
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Cover image for Business Listing / Advertising Platform
Business Listing / Advertising Platform
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Cover image for Sports Event Booking App
Sports Event Booking App
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Software Engineer(2)
Anurag Nagare's avatar
Anurag Nagare
Mumbai, India
I’m an AI & Machine Learning engineer with expertise in deve
I’m an AI & Machine Learning engineer with expertise in deve
Cover image for CricVision AI - Advanced Cricket Analytics Dashboard! CricVi...
CricVision AI - Advanced Cricket Analytics Dashboard! CricVision AI is an intelligent cricket analytics platform that leverages machine learning to provide real-time match predictions and comprehensive player insights. The dashboard predicts wicket probability, expected runs per ball, and boundary likelihood using three trained ML models with StandardScaler normalization. It features interactive match scenarios (Powerplay, Middle, and Death overs), over-by-over projections, win probability calculations, and economy rate forecasts. Users can compare players head-to-head, analyze form trends over recent innings, visualize run distribution through wagon wheels, and get AI confidence scores for all predictions - making it a complete solution for cricket enthusiasts and analysts.
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Cover image for I created WealthWise Agent, a smart personal finance planner...
I created WealthWise Agent, a smart personal finance planner designed to craft personalized budget plans and investment strategies. This app takes into account user inputs like salary, expenses, and financial goals, and then uses a Large Language Model (Gemini) to analyze these factors based on the 50/30/20 budgeting rule. It offers a clear step-by-step reasoning log, a detailed JSON-structured financial plan, and an interactive visualization of budget allocation, empowering users to make informed choices to reach their financial goals. 💻 Tech Stack Used: Frontend/UI: Gradio (custom themed with CSS, Orbitron font) AI/Logic: Google Gemini (gemini-1.5-flash) with LangChain agents Data: yFinance API for real-time stock/ETF data, Pandas & NumPy for calculations Visualization: Plotly Express for interactive charts
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Cover image for Everyone's talking about quantum computing.
Everyone's talking about quantum computing. Nobody's using it to feed farmers. India loses 20–30% of its crop yield every year to diseases and pests. Not because farmers don't care — but because early detection is hard, expensive, and inaccessible to the people who need it most. The existing solutions? Either a basic image classifier trained on lab-perfect photos that fail in real field conditions, or an agronomist visit that costs time and money most small farmers don't have. So I built QuantumEdge AgriGuard — a hybrid Quantum Neural Network app where a farmer can photograph a diseased leaf on their phone and get an instant diagnosis in under 5 seconds. Here's what makes it different from just another plant disease detector: Instead of a pure classical CNN, I built a hybrid architecture — a ResNet/EfficientNet backbone extracts visual features, then passes them into a Variational Quantum Circuit (VQC) for the final classification. The quantum layer uses angle embedding + StronglyEntanglingLayers, which gives it a measurable edge on small, noisy datasets — exactly the kind of data you get from Indian field conditions. The app doesn't just tell you what disease it is. It gives you: → Confidence score → Organic + chemical remedies (India-specific) → Yield impact estimate → A live classical vs quantum accuracy comparison so you can see the difference yourself I tested the quantum advantage claim honestly — ran both models on the same downsampled PlantVillage dataset and tracked accuracy, F1-score, and inference time side by side. The results are on the dashboard. No hand-waving. Built with PennyLane + PyTorch + Plotly Dash. Designed to run on simulators today and on QpiAI-Indus 25-qubit hardware tomorrow.
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Cover image for Most AI research tools are
Most AI research tools are just a chatbot with a search button. I built something different. Every time you ask an AI to research something, you're getting one model, one pass, no quality check. It writes confidently, cites poorly, and you have no idea if what it produced is actually accurate. For anyone making real decisions from AI-generated research, that's a silent risk most people ignore. The problem gets worse at scale the longer and more complex the question, the more a single model hallucinates, misses sources, and loses structure. There's no one checking its work. So I built ResearchOS a 5-agent pipeline where each agent has one job. A Supervisor breaks down your question. A Researcher runs parallel searches across 22+ sources. An Analyst extracts data and auto-generates charts. A Writer synthesises a cited report. A Critic fact-checks it and sends it back for revision if anything is wrong. The loop runs up to 3 times before the report is approved. One question in. A full cited report with charts and PDF export in under 10 minutes. I tested it live by watching the Critic catch a missing citation mid-run and send the Writer back to fix it before approval. That's the part that makes this actually usable for real work. Built on LangGraph, Groq, Tavily, ChromaDB and runs entirely on free tiers.
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Software Engineer(2)
Deion Dsouza's avatar
Deion Dsouza
Kolhapur, India
Software Engineer building user friendly software
Software Engineer building user friendly software
Cover image for AI-Powered Waste Segregation and Management System
AI-Powered Waste Segregation and Management System
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Cover image for Real-Time Bus Crowding Monitoring System
Real-Time Bus Crowding Monitoring System
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Cover image for Oasis - Event Management System
Oasis - Event Management System
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Software Engineer(3)
Prathamesh Bhosale's avatar
Prathamesh Bhosale
Mumbai, India
Affordable & Precise Software Solutions
Affordable & Precise Software Solutions
Cover image for API Server for my NGWebseries Project
API Server for my NGWebseries Project
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Cover image for prathamesh-bhosale/MyRemoteDesk
prathamesh-bhosale/MyRemoteDesk
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Cover image for prathamesh-bhosale/NgWebSeries
prathamesh-bhosale/NgWebSeries
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Software Engineer(3)