Freelance AI Engineers in DelhiFreelance AI 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
    128
    Followers
TOP_INDEPENDENT

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Versatile Fullstack Engineer | Web & Mobile Expert
Cover image for Légacie Phase 1
Légacie Phase 1
0
26
Cover image for Realm - Social Media Platform for Music Producers and Artists
Realm - Social Media Platform for Music Producers and Artists
1
161
Cover image for TestBest | LSAT® Prep & Tutoring
TestBest | LSAT® Prep & Tutoring
2
177
Cover image for AI Platform Stabilization and Enhancement for Kaie
AI Platform Stabilization and Enhancement for Kaie
3
123
AI Engineer(1)
Wahid Ali's avatar
Wahid Ali
proGhaziabad, India
Full-Stack Developer for SaaS, AI & Marketplace MVPs
    $10k+
    Earned
    5x
    Hired
    4.9
    Rating
    30
    Followers
Full-Stack Developer for SaaS, AI & Marketplace MVPs
Cover image for AI Search Optimization Platform
AI Search Optimization Platform
1
41
Cover image for Innovation Intelligence Platform
Innovation Intelligence Platform
1
48
Cover image for Flight Booking Platform
Flight Booking Platform
1
43
Cover image for Digital Business Card Platform
Digital Business Card Platform
1
42
AI Engineer(2)
Satya Prakash's avatar
Satya Prakash
proNew Delhi, India
Mobile App Architect • React Native Expert • 50+ App Shipped
    $5k+
    Earned
    1x
    Hired
    5.0
    Rating
    26
    Followers
Mobile App Architect • React Native Expert • 50+ App Shipped
Cover image for GymCare Management System Development
GymCare Management System Development
0
21
Cover image for Stellar Baby Mobile Application
Stellar Baby Mobile Application
1
49
Cover image for Rizz AI: Image Generation App Development
Rizz AI: Image Generation App Development
1
22
Cover image for Voice-First Productivity App Development
Voice-First Productivity App Development
1
17
AI Engineer(5)
Raj Pathak's avatar
Raj Pathak
proGurugram, India
Founding AI Engineer, Agents, Automation, Cloud, Full-Stack
    $1k+
    Earned
    9x
    Hired
    4.7
    Rating
    58
    Followers
Founding AI Engineer, Agents, Automation, Cloud, Full-Stack
Cover image for Automated Voice-over Workflow for Content Production
Automated Voice-over Workflow for Content Production
0
16
Cover image for AI Race Photography Platform Development
AI Race Photography Platform Development
0
19
Cover image for Dynamic Survey Reporting & Automation
Dynamic Survey Reporting & Automation
1
28
Cover image for WooCommerce to Shopify Migration & Custom Shopify Development
WooCommerce to Shopify Migration & Custom Shopify Development
1
25
AI Engineer(3)
Ajay Bidyarthy's avatar
Ajay Bidyarthy
Delhi, India
Python, CRM, AI Automation, Machine Learning and AI Engineer
    17
    Followers
Python, CRM, AI Automation, Machine Learning and AI Engineer
Cover image for The project focuses on automating
The project focuses on automating vehicle insurance certificate workflows using a scalable microservices architecture. The solution streamlines insurance certificate processing by reducing manual intervention, improving data handling, and enabling seamless communication between services through APIs and system integrations. The architecture is designed to support efficient workflow execution, reliable data exchange, and scalability as transaction volumes increase. By modernizing insurance operations, the solution helps create a more consistent, maintainable, and efficient process for managing vehicle insurance certificates. Business Impact 1. Increased Operational Efficiency: Reduced repetitive manual tasks and streamlined certificate processing workflows. 2. Faster Processing: Improved workflow execution to support quicker handling of insurance certificate requests. 3. Scalability: Enabled independent services to scale as business demand and transaction volumes grow. 4. Improved Reliability: Supported better service isolation and maintainability through a modular microservices architecture. 5. Reduced Operational Overhead: Helped minimize manual effort and workflow complexity. 6. Enhanced Integration: Enabled smoother communication between systems through APIs and service-to-service interactions. 7. Business Growth Readiness: Established a flexible foundation for future automation, integrations, and enhancements. Overall Outcome The project demonstrates how workflow automation and scalable microservices can modernize vehicle insurance operations, improve process efficiency, and provide a flexible technical foundation for future business growth.
0
24
Cover image for The solution streamlines insurance certificate
The solution streamlines insurance certificate processing by reducing manual intervention, improving data handling, and enabling seamless communication between services through APIs and system integrations. Overall Outcome The project demonstrates how workflow automation and scalable microservices can modernize vehicle insurance operations, improve process efficiency, and provide a flexible technical foundation for future business growth.
0
21
Cover image for A multi-tenant architecture is a
A multi-tenant architecture is a software architecture where a single application instance serves multiple customers (tenants) while keeping each tenant's data and configuration isolated. Example Imagine a SaaS CRM platform: - Company A uses the CRM. - Company B uses the same CRM. - Company C uses the same CRM. All three companies share the same application, but: -> Their users only see their own data. -> Their settings, branding, and permissions can be different. -> The platform provider maintains only one codebase.
2
205
Cover image for AI Chatbot Application Development
AI Chatbot Application Development
8
1.1K
AI Engineer(4)
Swastik Mangla's avatar
Swastik Mangla
New Delhi, India
AI Voice Agents for US Mortgage & Real Estate | Vocorix
AI Voice Agents for US Mortgage & Real Estate | Vocorix
Cover image for Stop Prompting, Start Orchestrating: The
Stop Prompting, Start Orchestrating: The 2026 Freelance Shift The "AI-assisted freelancer" is now a standard baseline. To stay in the top 1% on Contra this year, you need to transition into an AI Orchestrator. We are seeing a massive shift toward Multi-Agent Systems (MAS). Instead of you manually prompting a tool to write a caption, then another to generate an image, 2026 is about building "Agentic Workflows" that handle the entire project lifecycle autonomously, with you as the creative director. Top independent pros are now using specialized agents that don't just "write" proposals but analyze a client's past project history and "agent-readiness" to pitch automated solutions. Clients are no longer paying for "AI-generated" work; they are paying for verified orchestration. The value is in your ability to govern the AI’s output and ensure it meets brand-specific compliance. The "Shadow AI" Cleanup: Enterprise clients are moving away from messy, unmanaged AI tools. They want freelancers who can integrate into their governed AI Gateways. Don’t just sell a deliverable. Sell a self-optimizing system that saves your client time.
0
45
Cover image for Most "omni-channel" AI agents are
Most "omni-channel" AI agents are just disconnected silos. If a lead chats with you on the web and then calls 10 minutes later, the agent asks for their name and scenario all over again. Friction = Canceled Bookings. My client was spending $25k+ on PPC and losing high-value real estate leads at that handoff point. I built a frictionless Omni-Channel Real Estate AI Agent with Instant State Transfer. The AI agent knows who you are, what you were looking for, and where you left off, regardless of whether you are chatting or calling. The Tech Stack:  1️⃣ Context Cache (Supabase): I used Supabase as a persistent, real-time database to act as the AI’s central nervous system. 2️⃣ Web Chat (OpenAI API/React): The lead inputs property details (e.g., property value, current rate). 3️⃣ n8n Orchestration: n8n listens to the chat completion, sanitizes the data, and appends it to the Supabase context. 4️⃣ Voice (Retell AI / ElevenLabs v2.5): If the user calls, n8n detects the caller ID, performs an instant lookup in Supabase, and pre-loads the context into the agent’s prompt. The "Aha!" Moment: When the lead calls, the AI opens with: "I see we were just chatting about refinancing your $600k property on Oak Street. I have your info. Can I schedule that specialist call for tomorrow?" Conversion rate for refinancing inquiries doubled. No generic questions. No manual data entry. Just instant, intelligent qualification and booking. Skills: #OmniChannelAI #Supabase #n8n #RetellAI #OpenAI #APIIntegration #BuildInPublic #RealEstateAutomation
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63
Cover image for I built an AI engine
I built an AI engine that does the work of an entire creative team. You give it a single product photo. The system then analyzes the visual details, brainstorms creative concepts, and automatically generates high resolution images and cinematic videos. It even handles the file storage and database syncing for you. I am turning complex, hours long design cycles into a hands free operation. If you are looking to scale your content without the overhead, let’s build something together. #AIContentCreator #aivideo #freelancerlife #AutomationEngineers #n8n #CreativeAI #FLORA #brandvisuals
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31
Cover image for One of my financial consulting
One of my financial consulting clients had a massive problem: their AI voice agent, training on 50+ diverse funding programs, was starting to hallucinate. They were packing everything into one prompt, making it slow and unreliable. I offloaded all that program data into an external Knowledge Base (RAG), integrated seamlessly via n8n. Now, when a customer asks a complex question, the agent performs a micro-lookup, retrieves the exact, verified program details, and answers with 100% precision. Her core prompt is 90% shorter, reducing token cost and latency. The result isn't just a friendly bot, it’s a reliable expert in real-time. Short Skills: #RetellAI #n8n #RAG #KnowledgeBaseIntegration #PromptEngineering #AIVoice #AIHallucinationMitigation #FinancialAutomation #ConversationalDesign
0
68
AI Engineer(2)
Dishant Raghav's avatar
Dishant Raghav
New Delhi, India
Production AI Eng. | RAG • Agents • Observability • LLMs
New to Contra
Production AI Eng. | RAG • Agents • Observability • LLMs
Cover image for Knowledge-Base AI Assistant and Multi-Cloud GenAI Tools
Knowledge-Base AI Assistant and Multi-Cloud GenAI Tools
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4
Cover image for AI Integration with MediumMCP for Publishing
AI Integration with MediumMCP for Publishing
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4
Cover image for MCP Server Starter: Production-Ready TypeScript Template
MCP Server Starter: Production-Ready TypeScript Template
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2
Cover image for Lessons in Designing MCP Tool Names and Descriptions
What I learned building MCP servers for LinkedIn, Medium, Kaggle and X Over the past year I've open-sourced a series of MCP servers that let AI assistants like Claude work directly with LinkedIn, Medium, Kaggle and X. The biggest lesson had nothing to do with the APIs. It was how the tools are described. Three things made the clearest difference to whether an agent picked the right tool: - Action-first names like search_posts or get_profile, instead of vague ones like posts or query. - Descriptions that say when to use a tool, not just what it does. - Short, precise parameter docs with examples of valid values. With those changes, wrong tool calls dropped noticeably and agents needed far fewer retries. If you're adding AI agents or LLM features to your product and want them to work reliably in production, I'd be glad to help. Code: github.com/Dishant27
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68
AI Engineer(2)