Freelance Software Engineers in DelhiFreelance Software Engineers in Delhi
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AKASH VASHISHTHA's avatar
AKASH VASHISHTHA
proDelhi, India
Versatile Fullstack Engineer | Web & Mobile Expert
$50k+
Earned
5x
Hired
5.0
Rating
118
Followers
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Versatile Fullstack Engineer | Web & Mobile Expert
Cover image for Realm - Social Media Platform for Music Producers and Artists
Realm - Social Media Platform for Music Producers and Artists
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147
Cover image for Madklubben Online Reservation Modernization
Madklubben Online Reservation Modernization
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24
Cover image for Bento Station's Commission-Free Delivery Suite
Bento Station's Commission-Free Delivery Suite
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37
Cover image for Billing Crow
Billing Crow
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81
Software Engineer(4)
Suyash Dubey's avatar
Suyash Dubey
Delhi, India
I build production AI agents that automate real workflows
I build production AI agents that automate real workflows
Cover image for Built an LLM-powered question-answering application
Built an LLM-powered question-answering application that lets users ask natural-language questions over large document corpora and get accurate, grounded answers, instead of manually searching through documents for the right section. Designed and built the full RAG pipeline independently, from document ingestion through to answer generation, as a technical demonstration of production-grade retrieval-augmented generation using AWS-native tooling. Key Challenges: Documents exceeding token limits: Large source documents couldn't be fed directly into the LLM's context window, so they had to be broken down without losing meaning or context across chunks. Finding the right context: With a large corpus, the system needed to reliably surface the specific chunks relevant to a given question, not just the most textually similar ones. Grounded, accurate answers: Answers had to be based on the actual retrieved content, not the model's general knowledge, to avoid confidently wrong responses. Working within a managed AWS ecosystem: Embeddings, storage, and generation all needed to work together cleanly using Bedrock-native models rather than a patchwork of external services. Approach: Document loading and chunking Processed large documents into manageable chunks sized to stay within model token limits while preserving enough context for coherent retrieval. Vector embeddings with Amazon Titan Generated vector embeddings for each document chunk using Amazon Titan, capturing semantic meaning rather than just keyword overlap. Vector storage and retrieval Stored the embeddings in a vector database, enabling fast similarity search to pull the most relevant chunks for any given question. RAG-based answer generation with Claude on Bedrock When a question comes in, the system retrieves the relevant chunks and passes them as context to Anthropic Claude via Amazon Bedrock, which generates an answer grounded in the retrieved content rather than relying on parametric memory alone. Results & Impact: Accurate, source-grounded answers over document corpora too large to fit in a single context window. A scalable retrieval architecture that separates document processing, embedding, and generation, so any of the three can be swapped or scaled independently. A fully AWS-native RAG pipeline, demonstrating fluency with Bedrock's embedding and generation models working together in production patterns. Tech Stack Python · LangChain · AWS Bedrock · Amazon Titan · Anthropic Claude · FAISS DB
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Cover image for Built a virtual try-on application
Built a virtual try-on application that lets users try on garments over WhatsApp. A user sends a photo through WhatsApp, and the app returns a realistic image of them wearing the selected garment, no app download or website visit required. Designed and built the system end-to-end, from the WhatsApp messaging integration through to the try-on generation pipeline, as a self-contained product demonstrating conversational commerce for fashion/retail use cases. Key Challenges: - Frictionless UX over a messaging app: WhatsApp isn't built for structured app interactions, so the flow had to feel natural through simple image and text messages, not clunky commands. - Reliable image handling: Incoming photos vary wildly in quality, lighting, and pose, and had to be received, processed, and matched with garment images reliably. - Fast turnaround: Users expect a near-instant reply on a messaging app, so the backend had to handle image processing and model inference without long delays. - Stitching third-party services together: Twilio's WhatsApp API and Gradio's try-on model weren't built to talk to each other, so the app had to bridge them cleanly. Approach: WhatsApp integration via Twilio -Set up Twilio's WhatsApp API to receive incoming user images and send outgoing try-on results, handling the messaging layer end-to-end. Flask backend as the orchestration layer -Built a Flask application to receive Twilio webhooks, manage the request flow, and coordinate between incoming user images and the try-on model. Virtual try-on generation with Gradio - Integrated Gradio's virtual try-on model to generate the final garment-on-user image, returning a realistic composite result. End-to-end flow design - Connected the pieces so a user's WhatsApp message triggers the full pipeline automatically: receive image → process → generate try-on → send result back, all within a single conversation. Results & Impact - A working conversational shopping experience built entirely on a messaging app users already have open every day. - Zero-download, zero-signup try-on flow — removes the biggest friction point in getting users to try a new AI-powered feature. - A reusable integration pattern connecting Twilio, Flask, and a generative vision model, applicable to other WhatsApp-based commerce or personalization tools. Provided Services & Solutions 📌 Conversational App Development 📌 WhatsApp API Integration (Twilio) 📌 Backend Development (Flask) 📌 Generative AI Integration (Gradio virtual try-on model) 📌 Third-Party API Orchestration Tech Stack: Python · Flask · Twilio WhatsApp API · Gradio If you want an AI-powered experience built directly into a channel your customers already use, like WhatsApp, let's talk.
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Cover image for Built and maintained a HIPAA
Built and maintained a HIPAA compliant production medical AI scribe system that uses multi-step LLM agents to extract clinical entities directly from physician-patient conversations and turn them into structured medical notes, cutting down the manual transcription work clinicians used to do after every visit. Multi-stage clinical workflow: Documentation, coding, and review each have different logic and conditional paths, and the system needed to branch correctly between them without losing context. Clinical accuracy: Generated notes had to be grounded in real patient history and clinical guidelines, not just plausible-sounding text. Production reliability: As a live system handling real conversations, every agent run needed to be observable, debuggable, and monitored for cost and latency in real time. Non-technical requirements gathering: Clinical needs had to be captured accurately from stakeholders without a technical background and translated into precise agent logic. Approach: Stateful agent orchestration with LangGraph Designed LangGraph-based agent workflows with conditional branching, allowing the system to move correctly across documentation, coding, and review stages based on conversation content. Context-grounded note generation with RAG Built a RAG pipeline on AWS Bedrock with embeddings, so every generated note is grounded in the patient's actual history and relevant clinical guidelines rather than generic output. Full production observability Integrated Langfuse across all agent runs to track token usage, latency, and model KPIs, giving the team visibility into system health and cost in production, not just at build time. Clinical stakeholder collaboration Ran requirements sessions directly with clinical staff, converting their documentation needs into concrete agent behavior specs and validation criteria. Results & Impact: ~40% reduction in manual transcription time for clinicians using the system. Clinically grounded output, with notes tied to real patient history and guidelines instead of unsupported generation. Full production observability, with token usage, latency, and model performance tracked continuously. A workflow clinicians could trust, built through direct collaboration rather than a black-box handoff. Provided Services & Solutions: 📌 AI Agent Development (LangGraph) 📌 RAG Pipeline Development (AWS Bedrock) 📌 LLM Observability (Langfuse) 📌 Cloud Infrastructure (AWS Lambda, S3, DynamoDB) 📌 Stakeholder Requirements Translation 📌 Production ML Systems Tech Stack Python · LangChain · LangGraph · AWS (Bedrock, Lambda, S3, DynamoDB) · Langfuse · TypeScript · REST APIs
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87
Cover image for Overview 📖 Built an end-to-end agentic
Overview 📖 Built an end-to-end agentic content creation pipeline for a fast-growing AI-powered SEO platform. The system chains multiple LLM agents together to research, draft, and optimize content automatically, replacing what used to be a manual, multi-step editorial process with a single automated workflow. Collaboration 🤝 Partnered directly with the platform's engineering team to design and ship the automation layer that now sits at the core of their content operations, turning a bottlenecked manual process into a scalable, always-on pipeline. Key Challenges 🤔 Multi-step content logic: Research, drafting, and optimization each require different context and tone, but had to feel like one coherent pipeline, not three disconnected tools. Consistency at scale: Every piece of generated content had to match brand voice and pass compliance checks, without a human reviewing each one manually. Orchestration complexity: Content jobs needed to trigger reliably from webhooks and third-party APIs, run through multiple agents in sequence, and fail gracefully without stalling the whole pipeline. Performance under load: The backend had to stay fast and stable as content throughput scaled up. Approach 🔍 Agentic content pipeline design Designed a multi-step LangChain agent chain with tool-calling, where each agent (research, drafting, optimization) has a clearly scoped role and hands off structured output to the next. Workflow orchestration with n8n Built n8n automation workflows to handle webhook triggers, third-party API integrations, and job routing, removing the need for manual intervention at almost every stage. Brand voice & compliance enforcement Layered in structured prompting and validation steps so generated content stays on-brand and passes compliance checks automatically, at scale. Backend performance tuning Optimized FastAPI endpoints and managed Azure-hosted PostgreSQL databases to keep latency low under high content-throughput conditions. Results & Impact ✨ ~60% reduction in manual intervention across the content pipeline, freeing the team to focus on strategy instead of babysitting workflows. Consistent brand voice at scale, with compliance checks running automatically instead of manually. Reliable, low-latency infrastructure validated under real content-throughput loads. A reusable agentic architecture the platform can extend to new content types without rebuilding the pipeline. Provided Services & Solutions ✅ 📌 AI Agent Development (LangChain) 📌 Workflow Automation (n8n) 📌 LLM Integration (GPT-4, Claude) 📌 API Development (FastAPI) 📌 Cloud Database Management (Azure, PostgreSQL) 📌 Architecture Design & Consulting Tech Stack Python · FastAPI · LangChain · n8n · GPT-4 · Claude · Azure · PostgreSQL
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88
Software Engineer(4)
SUJEET KUMAR's avatar
SUJEET KUMAR
New Delhi, India
Custom Software | ERP • POS • Weighbridge • Royalty
New to Contra
Custom Software | ERP • POS • Weighbridge • Royalty
Cover image for Restaurant POS & Billing Management
Restaurant POS & Billing Management System Designed and developed a complete Restaurant POS & Billing Management System to streamline restaurant order management, billing, kitchen operations, inventory, and delivery workflows. The system provides a fast and intuitive interface for managing dine-in, takeaway, and delivery orders, with category-based menu browsing and real-time order processing. Key Features: 🍽️ Restaurant POS & Place Order 🧾 Automated restaurant billing 🚚 Dine-in, Takeaway & Delivery management 👨‍🍳 Kitchen Display System (KDS) 📦 Inventory & stock management 📊 Restaurant reports & analytics 💰 GST & tax calculation 🏪 Restaurant entries & master management 📱 Delivery customer & delivery-boy management 🧮 Discounts, service charges, GST & packing charges 🔐 Admin & role-based management ⚡ Fast, responsive POS interface
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101
Cover image for Custom Business Software & ERP
Custom Business Software & ERP Solutions Platform Designed and developed a modern, responsive business software platform for CVSS (Custom Software Solutions) to showcase and promote customized software solutions for businesses. The platform presents an integrated suite of solutions including ERP, Billing & GST, Weighbridge Management, Restaurant POS, Hotel Management, business dashboards, API integrations, and custom software development. The website is designed with a clean, professional UI to clearly communicate complex software solutions, highlight key features, and encourage businesses to book a free demo or connect via WhatsApp. Key highlights: • Modern responsive business website • Custom ERP & business software presentation • Weighbridge & industrial software solutions • Restaurant & Hotel management solutions • Dashboard-focused UI/UX • API & third-party integrations • WhatsApp lead-generation integration • Hosting & deployment support • Conversion-focused landing page design
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93
Cover image for Uttarakhand Royalty & Weighbridge Management
Uttarakhand Royalty & Weighbridge Management System Developed a real-time Royalty & Weighbridge Management System for mineral transportation operations in Uttarakhand, integrating government mining/royalty services with automated weighbridge verification. The system verifies vehicle, material and weight information using multiple weighbridge cameras, captures first and final weight images, calculates gross, tare and net weight, and manages royalty-related data before submission. Key features: Government royalty / mining API integration Uttarakhand e-Rawanna / e-Transit workflow integration Real-time vehicle verification Automated weighbridge data processing First-weight & final-weight verification Multi-camera image capture and verification Gross, tare & net weight calculation Vehicle and mineral/material tracking Token-based transaction management Lessee & weighbridge configuration Royalty data submission Branch-wise operational management Audit-ready transaction records
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104
Cover image for Stone Crusher Management & Weighbridge
Stone Crusher Management & Weighbridge ERP Designed and developed a complete Stone Crusher Management & Weighbridge ERP system for managing real-time vehicle operations, token entry, weighbridge transactions, billing, invoices, receipts, and operational reports. Key features: • Real-time vehicle & weighbridge monitoring • Token generation and vehicle tracking • Loading, pending & completed vehicle management • Digital weighbridge / Kanta slip management • Invoice, billing & GST management • Receipt generation • Reports and operational analytics • Multi-user and role-based access • Responsive web dashboard • Database-driven business management Technology: Java, Spring Boot, React, MySQL, REST APIs, Hibernate/JPA
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91
Software Engineer(4)
Anil Singh's avatar
Anil Singh
Delhi, India
Full Stack Engineer building Fast Websites and Web Apps
27
Followers
Full Stack Engineer building Fast Websites and Web Apps
Cover image for E-Commerce Platform – Shopsphere
E-Commerce Platform – Shopsphere
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10
Cover image for Startup Discovery & Pitch Platform
Startup Discovery & Pitch Platform
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9
Cover image for The 108 Peaks – Website Development
The 108 Peaks – Website Development
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12
Cover image for Modern SaaS Website UI – Xora
Modern SaaS Website UI – Xora
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9
Software Engineer(2)
Ritik Goyal's avatar
Ritik Goyal
Delhi, India
Python & Django developer for web apps and APIs
New to Contra
Python & Django developer for web apps and APIs
Cover image for Spent an afternoon this week
Spent an afternoon this week chasing down why a client's order dashboard took around 2.5s to load. Turned out the page was firing 300+ database queries. Classic N+1 problem. Every order in the loop was making its own separate trip to the DB just to grab the customer and product. One line fixed it. select_related() tells Django to pull the related rows in a single JOIN instead of querying them one at a time. 312 queries down to 3. Page load went from 2.4s to 0.08s. The annoying part is this never shows up on small datasets in dev. It only bites you in production once the table grows. So now I always check the query count in Django Debug Toolbar before shipping any list view. What's the worst N+1 you've run into?
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Cover image for Hit a classic Django trap
Hit a classic Django trap this week and figured it's worth sharing since so many people are running into it now. I was adding an LLM feature to a Django app. The AI call takes a few seconds, so naturally I made the view async so it doesn't block a worker while waiting. Wrote the async view, called the ORM like I always do, and boom: SynchronousOnlyOperation: You cannot call this from an async context. Turns out Django's ORM can't just be called normally inside async code. The classic sync API isn't safe in an event loop, so Django protects it and throws this error instead. The fix is simpler than most people think. Since Django 4.1 the ORM has async versions of everything, same names with an "a" prefix. So objects.get() becomes await objects.aget(), create() becomes acreate(), save() becomes asave(). For loops over querysets, async for works directly. And for old sync code or third party libraries you can't change, wrap them with sync_to_async(). Why this matters right now: everyone is bolting AI features onto Django apps, and LLM calls are exactly the slow I/O that async is made for. Which means a lot of devs who never touched async Django are suddenly hitting this error for the first time. One honest caveat: transactions still don't fully work in async mode, so if you need atomic blocks, keep that path sync and wrap it. Anyone else made the jump to async views yet, or still happily on WSGI?
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Cover image for An AI-driven trading engine that
An AI-driven trading engine that analyzes 200-DMA breakouts and real-time market sentiment to generate long/short recommendations. It scans 1,500+ NSE stocks in under 2 seconds and uses generative AI to build option strategies, delivering event-driven trade signals end-to-end. I built the full Django backend, async APIs, and the signal-generation logic. Accomplishments and responsibilities: Built an AI-driven trading engine analyzing 200-DMA breakouts and market sentiment, generating long/short signals with ~70% directional accuracy — outperforming baseline strategies by 35%; Integrated generative-AI insights for automated option-strategy creation (spreads, straddles, condors), improving Sharpe ratio by 1.6× and cutting manual analysis time by 60%; Developed a Django backend with async APIs scanning 1,500+ NSE stocks in under 2 seconds, achieving 40% lower latency with event-driven alerts.
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Cover image for An AI-powered news platform that
An AI-powered news platform that delivers concise, real-time AI-industry updates to 2,500+ active users. It scrapes and aggregates 500+ sources daily, removes duplicates, and uses generative-AI summarization to cut reading time significantly while surfacing the most relevant stories. I built the backend responsible for scraping, deduplication, and the LLM summarization pipeline.
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Software Engineer(6)
Prajjwal Barmaiya's avatar
Prajjwal Barmaiya
Delhi, India
punctual, motivated, hardworker, Quick Learner
punctual, motivated, hardworker, Quick Learner
Cover image for Music-Player-Using-HTML-And-CSS
Music-Player-Using-HTML-And-CSS
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9
Cover image for Automatic Attendance System with Face Recognition
Automatic Attendance System with Face Recognition
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6
Cover image for RISC-V Based Single-Cycle Core Design
RISC-V Based Single-Cycle Core Design
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4
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Software Engineer(2)