Freelancers using FastAPI in BengaluruFreelancers using FastAPI in Bengaluru
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Sriharsan BS's avatar
Sriharsan BS
Bengaluru, India
Production ML that turns messy data into decisions!!
New to Contra
Production ML that turns messy data into decisions!!
Cover image for NeuroVision AI is a clinical-grade
NeuroVision AI is a clinical-grade platform that detects neurological gait disorders from walking videos using computer vision and AI. It extracts key biomechanical parameters - cadence, symmetry, tremor index, and freezing episodes, via MediaPipe pose estimation and Isolation Forest anomaly detection, trained on established datasets including DaphNet and PhysioNet GaitPDB. The system delivers AI-powered differential diagnoses, real-time clinical Q&A, and spoken voice reports, all within 60 seconds of video input.
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Cover image for VitalFlow HeartGuard is a cardiovascular
VitalFlow HeartGuard is a cardiovascular risk analytics platform built on the Framingham Heart Study dataset (4,238 patients). It trains three Apache Spark ML models — Logistic Regression, Random Forest, and Gradient Boosted Trees — to predict 10-year coronary heart disease risk. The platform features a FastAPI backend with JWT auth, live what-if analysis, counterfactual interventions, model drift detection, and PDF report generation. A React dashboard visualizes EDA, ROC curves, feature importance, and patient risk trends.
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Cover image for Day one on Contra. New
Day one on Contra. New to the platform, not to the work. I build production ML systems that turn messy data into decisions, and I build the product around them. Where I am strongest: Production ML: risk scoring, prediction, ranking, computer vision Data engineering: pipelines that turn raw, scattered data into reliable features LLM apps: RAG, structured prompting, and evaluation that keeps them honest And I built the full product around it, too: FastAPI and backend services, React and Next.js frontends, dashboards, mobile, and workflow automation when a project needs it. I like owning the whole path from data to a working thing people use. Most builders stop at the demo. I take it to be deployed, monitored, and actually earning its keep. I also provide automation services for your Saas that you don't wanna waste your hours on. Starting today, I am open to work. Independent contracts, remote, a full build, or one piece of one. Startups and small teams turning an idea into a real product are exactly who I want to work with. My projects are on my profile if you want to see how I think before we talk. A piece of my playground work done within hours is posted too. #MachineLearning #AIEngineer #LLM #FastAPI #React #Nextjs #Contra
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Cover image for TechnovaHub chatbot is a branded
TechnovaHub chatbot is a branded AI-powered customer assistant built with React and Node.js, featuring real-time streaming responses via Google Gemini API. It supports bilingual interaction (English and Tamil), voice input, lead capture, and smart intent detection for courses and pricing. The chatbot includes a secure backend proxy, XSS sanitization, and a modular architecture with 57 automated tests ensuring production-grade reliability.
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FastAPI(2)
Mirwaise Khan's avatar
Mirwaise Khan
Bengaluru, India
AI Product Engineer building custom LLM & RAG pipelines.
New to Contra
AI Product Engineer building custom LLM & RAG pipelines.
Cover image for Project Title:- Custom Enterprise RAG
Project Title:- Custom Enterprise RAG Assistant | Chat with Documents & PDFs Project Overview: DocuMind is an enterprise-grade Retrieval-Augmented Generation (RAG) assistant designed to eliminate manual data extraction and prevent AI hallucinations. It converts static corporate files (PDFs, reports, resumes, contracts) into an interactive, grounded knowledge engine that provides answers backed by direct page-level citations. The Problem Solved: * Manual Data Hunting: Eliminates hours spent reading through dense, complex documentation. AI Hallucinations: Constrains LLM outputs strictly to uploaded context, ensuring reliable, factual data. Lack of Auditability: Provides exact file names and page references for compliance and verification. Key Technical Features: * Dynamic Indexing: Fast chunking and local vector embedding using Hugging Face models (all-MiniLM-L6-v2). High-Accuracy Vector Search: ChromaDB integration for persistent vector storage and low-latency similarity retrieval. Decoupled Architecture: Asynchronous FastAPI backend paired with a clean, responsive Streamlit chat frontend. Contextual Synthesis: Powered by Google Gemini (gemini-2.5-flash) for cost-effective inference. Tech Stack: Python, FastAPI, LangChain, ChromaDB, Hugging Face, Google Gemini, Streamlit.
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Cover image for 🚀 How I Solved the
🚀 How I Solved the HR Resume Bottleneck with AI Recruiters waste endless hours manually scanning hundreds of unformatted resumes, often missing strong candidates due to fatigue. To solve this, I built an AI Resume Screening Agent that automates the heavy lifting: Semantic Matching: Uses sentence embeddings to understand the true context of skills rather than relying on rigid keyword matching. Instant Leaderboard: Automatically parses multiple PDF resumes, computes match scores, and ranks candidates from best to worst. Actionable AI Feedback: Instantly generates structured breakdowns of candidate strengths, missing skill gaps, and hiring recommendations using the Groq API. Workflow Efficiency: Condenses hours of sorting into seconds with a one-click CSV report export. 🛠 Tech Stack: Python, FastAPI, Groq, SentenceTransformers, Pandas, HTML/CSS. Check the comments for the live public link to test it yourself! What tools do you use to speed up hiring?
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Cover image for Hi, I'm Mirwaise Khan, an
Hi, I'm Mirwaise Khan, an AI engineer building practical tools with large language models. This is a project I built — an Enterprise AI Document Assistant. You upload any PDF, and it indexes the content using ChromaDB and vector embeddings. Then you can ask it questions in plain English, and it retrieves the exact relevant section and gives you a grounded answer — with the source cited, so you always know where the information came from. I build systems like this — RAG pipelines, AI chatbots, and document automation tools — using Python, LLMs, and vector databases. If you need something like this for your business, let's talk.
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Cover image for AI Invoice Data Extraction Dashboard Built
AI Invoice Data Extraction Dashboard Built an AI-powered tool that automatically extracts key data from PDF invoices — vendor name, invoice number, date, tax, and total amount — and displays it in an editable dashboard for quick review before saving. Users can upload any invoice PDF, get instant structured extraction, correct fields if needed, and export clean data straight to CSV. Tech used: Python, GenAI/LLM extraction, Pandas, PDF parsing, Streamlit/web UI
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FastAPI(2)
Umme Nusrath's avatar
Umme Nusrath
Bengaluru, India
Full-stack web developer — React, JS, Websites & modern UIs.
New to Contra
Full-stack web developer — React, JS, Websites & modern UIs.
Cover image for Built a full ecommerce fashion
Built a full ecommerce fashion storefront for AL Creations (abaya, hijab & custom wear) — homepage, shop filters, product pages, cart/checkout, WhatsApp ordering, and a client admin to manage products & stock. Designed for real small businesses who want an Amazon-style shopping experience without Shopify complexity. Stack: HTML, CSS, JavaScript, React-ready UI patterns, Supabase, Netlify. Live demo: https://al-creations-store-preview.netlify.app I help brands launch clean, mobile-friendly websites that convert visitors into orders.
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Cover image for Built Boomerang — a landing
Built Boomerang — a landing experience for building lasting relationships. Live demo: https://boomerang-sand.vercel.app/ (https://boomerang-sand.vercel.app/)video walkthrough: https://www.loom.com/share/e3d2389bed1e45e6ab0d04d99228ee92 #webdesign #frontend
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Cover image for SOFTDROP — soft-launch studio for
SOFTDROP — soft-launch studio for Gen Z creators. I built the product I wish existed: drop privately to your circle, collect real heat (would share / would buy / saves), plan soft vs public on Stack, then Forge a caption pack for TikTok / IG / Shorts — with Trend Radar that always says soft launch first. Flowstep prototype (public): https://app.flowstep.ai/file?activeFileId=35f205a1-d4b8-4027-8503-8200e6456a5b (https://app.flowstep.ai/file?activeFileId=35f205a1-d4b8-4027-8503-8200e6456a5b)LinkedIn post: https://www.linkedin.com/posts/share-7488847095685148672-x8Ja/?utm_source=share&utm_medium=member_desktop&rcm=ACoAACkVFsQBZOclokeQuU49ndgFqucqrfHAE1c Multi-screen clickable flow: Splash → Circle → Stack → Create → Heat → Forge → Aura → Radar. #flowstepchallenge
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Cover image for Designed and built shop listing,
Designed and built shop listing, category filters, and product detail pages for a fashion ecommerce store (abaya, hijab, kids & custom wear). Focus: clean UI, mobile-friendly layout, clear pricing, and fast path from browse → product → cart. watch- walkthrogh:https://www.loom.com/share/3f8f06558ae946698e8361f8e9681097 Live demo: https://al-creations-store-preview.netlify.app
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FastAPI(1)
Manohar Bhat's avatar
Manohar Bhat
Bengaluru, India
I build AI systems and cross-platform mobile apps.
I build AI systems and cross-platform mobile apps.
Cover image for LookInsight AI : AI Customer Support Automation System Full-...
LookInsight AI : AI Customer Support Automation System Full-stack AI system that automates customer support workflows. What it does: → Classifies customer intent (complaint, inquiry, refund, support) → Detects urgency and routes to the right team → Looks up customer history via knowledge graph → Generates personalized response drafts in seconds Key features: • Real-time processing pipeline with live visualization • Multi-channel support (Email, Twitter, Slack, WhatsApp) • Business rules engine for tier-based treatment (VIP, Premium, Regular) • Neo4j knowledge graph for customer context Tech stack: Python, FastAPI, OpenAI GPT-4, Neo4j, Next.js, TypeScript Live demo: https://lookinsight.ai Skills: Python AI OpenAI FastAPI Next.js Customer Support Automation Link: https://lookinsight.ai
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Cover image for Odegu - Real-Time Delivery Tracking Platform
Odegu - Real-Time Delivery Tracking Platform
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Cover image for CourtSlot - Community Court Booking App
CourtSlot - Community Court Booking App
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Cover image for SEC Filing Analyzer - AI-Powered Financial Analysis Platform
SEC Filing Analyzer - AI-Powered Financial Analysis Platform
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FastAPI(1)
Medhansh Singh's avatar
Medhansh Singh
Bengaluru, India
AIML @ RVCE | Building Private, High-Performance Local AI
AIML @ RVCE | Building Private, High-Performance Local AI
Cover image for Vision-Cart V2: Neural Intelligence Platform Developed
Vision-Cart V2: Neural Intelligence Platform Developed a high-end Visual Auditing dashboard for retail and industrial quality control. Unlike basic classifiers, this system performs Deep Feature Extraction—calculating metrics like caffeine content (mg), volume (ml), and shelf fill-levels. Edge-AI Mastery: Optimized to run Ollama (Moondream) locally on restricted hardware (2GB VRAM), ensuring total data privacy. Industrial UI: Built a 'Nuvion-Black' interface with real-time scanning animations and a horizontal Bento-grid for system vitals. Business Logic: Integrated a 'Neural Suggestion' engine to turn visual scans into automated inventory and QC decisions. Tags: #ComputerVision #EdgeAI #React #FastAPI #MachineLearning
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Cover image for The Culinary Hub: High-Performance, Commission-Free
The Culinary Hub: High-Performance, Commission-Free Cafe Engine Project Description The Problem: Local cafes lose ~30% revenue to aggregator commissions (Zomato/Swiggy). The Solution: A high-fidelity "Emerald Glass" storefront designed for direct customer conversion. Key Features: Zero-Commission Engine: Structured WhatsApp ordering to bypass third-party fees. Operational Logic: Real-time "Shop Status" toggle to manage kitchen hours automatically. Premium UI: Glassmorphism design built with Tailwind CSS for a mobile-first, luxury feel. Loyalty Focused: Built-in "Order Your Usual" feature via local storage for repeat customers.
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Cover image for SkyFlow | End-to-End Flight Engine The
SkyFlow | End-to-End Flight Engine The Goal: A high-performance booking journey from search to seat selection. The Tech: Powered by Python (Flask) and Amadeus API for real-time global flight data. Key Features: Interactive Seat Mapping & real-time price fetching. Automated Ticket Generation & booking management. "Soft-Luxury" UI designed with Tailwind CSS for mobile responsiveness. The Result: A seamless, professional-grade flow built for speed and reliability.
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Cover image for Premium Hospitality Landing Page Template The
Premium Hospitality Landing Page Template The Project: A high-performance, mobile-responsive landing page designed for the hospitality industry. Built with a focus on "Soft-Luxury" aesthetics and high-speed performance. Key Tech: HTML5, CSS3, JavaScript, and Netlify for deployment. Top Feature: Commission-free WhatsApp booking integration to drive direct customer reservations. Try it yourself: https://demo-cafes-123.netlify.app/
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FastAPI(1)
Sinchana T's avatar
Sinchana T
Bengaluru, India
Turning ideas into scalable web systems
Turning ideas into scalable web systems
Cover image for AI-Powered E-Waste Management Platform: Sustainable Tech Sol...
Project Goal: Designed and developed a full-stack, AI-powered e-waste management platform to promote responsible electronic waste disposal while enabling value recovery, recycling discovery, and convenient pickup services. • Built RESTful backend APIs using FastAPI / Node.js for authentication, classification workflows, facility management, pickup scheduling, and marketplace operations • Integrated an AI image classification model to categorize e-waste into recyclable, reusable, or hazardous types • Implemented a facility locator with map integration to identify nearby certified recycling centers • Developed a rule-based value estimation engine for resale and scrap pricing • Created a slot-based pickup scheduling system with confirmation flow • Integrated Razorpay payment gateway for secure online transactions
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Cover image for Audio / Text to Indian
Audio / Text to Indian Sign Language Converter Built a web-based accessibility application that converts audio and text into Indian Sign Language (ISL) to help improve communication for people with hearing impairments. The system uses the Web Speech API for speech-to-text conversion and applies NLP techniques to process and simplify text before mapping it to corresponding ISL gesture animations for clear visual output. Tech stack: Django, JavaScript, HTML, CSS, NLTK Focused on accessibility, inclusivity, and real-world usability.
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Cover image for File Management & Sharing Web
File Management & Sharing Web App Built a web-based application to upload, organize, manage, and share files securely with folder support. Key features: • Upload multiple files at once to folders or root • Create and delete folders dynamically • Download and delete files easily • Generate shareable links with expiration • Copy share links instantly to clipboard Cloud & backend: Files stored securely in AWS S3 Metadata managed in MongoDB Backend built with Node.js & Express Frontend: Responsive UI using React and Tailwind CSS Designed for scalability, secure file handling, and smooth user experience.
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FastAPI(1)
Vimal Anand's avatar
Vimal Anand
Bengaluru, India
Full Stack AI Engineer shipping fast SaaS MVPs
New to Contra
Full Stack AI Engineer shipping fast SaaS MVPs
Cover image for 𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲: Most Voice AI agents
𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲: Most Voice AI agents still feel like glorified walkie-talkies. So I built one that actually listens like a human. Most voice bots today follow a rigid, linear loop: You speak ➔ Wait ➔ Bot speaks. If you dare interrupt them mid-sentence? They either completely ignore you or their context buffer gets totally mangled. 𝗧𝗼 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗶𝘀, I engineered Astra Duplex Agent — a sub-50ms, ultra-low latency, full-duplex conversational Voice AI built to handle natural human interruptions seamlessly. Here is what went into building the architecture under the hood: True Full-Duplex WebSockets: Built on FastAPI, allowing real-time bi-directional audio streaming instead of turn-based HTTP requests. Edge Voice Activity Detection (VAD): Deployed Silero VAD using ONNX Runtime to catch user barge-ins at the edge with near-zero overhead. LangGraph State Machine: Instead of a simple monolithic script, the conversation logic is modeled as a state graph, giving precise control over execution flow. Partial State Tracking & Memory Persistence: This was the trickiest part. When an interruption happens, Astra doesn't just stop audio playback — an async kill-switch halts Groq's LLaMA-3.1 mid-token, calculates the exact partial sentence actually spoken by the TTS, and saves only that partial context to an Upstash Redis buffer. Sub-50ms Inference & Streaming: Leveraged Groq LPU (LLaMA-3.1 + Whisper Large v3) paired with ElevenLabs Turbo v2.5 streaming. The result? An agent that you can interrupt mid-thought, change topics with on the fly, and ask follow-up questions without it losing historical context. Containerized with Docker and live on Render & Vercel. Check out the full architecture & live demo below! https://lnkd.in/eC6yak3i (https://lnkd.in/eC6yak3i)https://lnkd.in/eN38HGZD (https://lnkd.in/eN38HGZD)#VoiceAI (https://www.linkedin.com/search/results/all/?keywords=%23voiceai&origin=HASH_TAG_FROM_FEED) #GenerativeAI (https://www.linkedin.com/search/results/all/?keywords=%23generativeai&origin=HASH_TAG_FROM_FEED) #LangGraph (https://www.linkedin.com/search/results/all/?keywords=%23langgraph&origin=HASH_TAG_FROM_FEED) #SystemDesign (https://www.linkedin.com/search/results/all/?keywords=%23systemdesign&origin=HASH_TAG_FROM_FEED) #FastAPI (https://www.linkedin.com/search/results/all/?keywords=%23fastapi&origin=HASH_TAG_FROM_FEED) #MachineLearning (https://www.linkedin.com/search/results/all/?keywords=%23machinelearning&origin=HASH_TAG_FROM_FEED) #WebSockets (https://www.linkedin.com/search/results/all/?keywords=%23websockets&origin=HASH_TAG_FROM_FEED) #Python (https://www.linkedin.com/search/results/all/?keywords=%23python&origin=HASH_TAG_FROM_FEED) #AIENGINEERING (https://www.linkedin.com/search/results/all/?keywords=%23aiengineering&origin=HASH_TAG_FROM_FEED)
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Cover image for 𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲: What started as a
𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲: What started as a simple B2C expense tracker has officially pivoted into a fully automated B2B Financial SaaS. Introducing 𝗙𝗶𝗻𝗮𝗻𝗦𝗺𝗮𝗿𝘁. 𝗕𝗼𝗱𝘆: I realized that startup founders and agency owners spend countless hours manually categorizing bank statements and tracking operational costs. I wanted to build a solution that entirely automates this bookkeeping process. Building the core feature—an "𝐀𝐈 𝐁𝐚𝐧𝐤 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭 𝐏𝐚𝐫𝐬𝐞𝐫"—was one of the toughest technical challenges I’ve faced. I hit a massive roadblock when Next.js 14’s Webpack bundler kept breaking legacy PDF OCR libraries in serverless environments. Instead of compromising on the feature, I completely re-architected the data pipeline: • I bypassed Webpack issues by implementing pdf2json on a strict Node.js runtime for secure, server-side text extraction. • I piped this raw text into Groq’s LLaMA-3.1-8b model using highly optimized system prompts. • The result? Lightning-fast, deterministic extraction that converts raw PDF text into perfectly structured JSON arrays. Now, users can drag-and-drop a PDF statement, and the AI instantly categorizes every transaction (Infrastructure, Payroll, Revenue) into a 'pending' staging area. Once approved, the data flows securely via Drizzle ORM into a Neon PostgreSQL database, updating real-time Recharts dashboards. 𝑻𝒆𝒄𝒉 𝑺𝒕𝒂𝒄𝒌: Next.js App Router, Tailwind CSS, Shadcn UI, Clerk Auth, Drizzle ORM, Neon DB, and Groq SDK. 𝗪𝗵𝗮𝘁'𝘀 𝗡𝗲𝘅𝘁? I am currently looking for my next full-time opportunity as a Full Stack Developer (based in Bangalore or anywhere). I am also actively taking on 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀. If you are a founder or agency looking to build scalable, AI-integrated SaaS MVPs without the technical headache, let's connect. Check out the demo video below to see the AI parser in action! 🔗 Live Project: https://lnkd.in/gSNu3cAK (https://lnkd.in/gSNu3cAK)hashtag#BuildInPublic (https://www.linkedin.com/search/results/all/?keywords=%23buildinpublic&origin=HASH_TAG_FROM_FEED) hashtag#Nextjs (https://www.linkedin.com/search/results/all/?keywords=%23nextjs&origin=HASH_TAG_FROM_FEED) hashtag#FullStackDevelopment (https://www.linkedin.com/search/results/all/?keywords=%23fullstackdevelopment&origin=HASH_TAG_FROM_FEED) hashtag#SaaS (https://www.linkedin.com/search/results/all/?keywords=%23saas&origin=HASH_TAG_FROM_FEED) hashtag#AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) hashtag#Groq (https://www.linkedin.com/search/results/all/?keywords=%23groq&origin=HASH_TAG_FROM_FEED) hashtag#PostgreSQL (https://www.linkedin.com/search/results/all/?keywords=%23postgresql&origin=HASH_TAG_FROM_FEED) hashtag#WebDevelopment (https://www.linkedin.com/search/results/all/?keywords=%23webdevelopment&origin=HASH_TAG_FROM_FEED) hashtag#FreelanceDeveloper (https://www.linkedin.com/search/results/all/?keywords=%23freelancedeveloper&origin=HASH_TAG_FROM_FEED) hashtag#Hiring (https://www.linkedin.com/search/results/all/?keywords=%23hiring&origin=HASH_TAG_FROM_FEED)
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Cover image for I recently architected and built
I recently architected and built VedaAI Assessment Creator, a personal project designed to transform unstructured notes and files (PDFs, DOCX) into highly structured, curriculum-aligned exam papers. Building enterprise-grade applications at scale requires looking beyond simply making API calls. For this build, I wanted to focus entirely on non-blocking architectures and deterministic AI outputs. Here is a breakdown of the technical decisions: Asynchronous Workers: Instead of blocking the main HTTP thread during heavy file parsing and AI inference, I implemented a distributed task queue using BullMQ and Upstash Serverless Redis. The Express API responds in under 100ms, while background workers handle the heavy lifting. Deterministic AI: I opted for Groq (Llama-3.3-70b) utilizing its JSON mode. The sub-500ms inference time and guaranteed schema compliance eliminated the need for complex post-processing validation. Real-Time Synchronization: A WebSocket setup broadcasts job completion events, updating the Next.js frontend instantly without relying on inefficient polling. Database & State: MongoDB Atlas handles ACID transactions for complex document updates, while Zustand manages lightweight, atomic state slices on the frontend. I also built in granular question regeneration—allowing users to re-run isolated inference calls for single questions without replacing the entire paper—and print-ready A4 PDF exports. Designing this kind of scalable infrastructure directly supports my ongoing deep dive into advanced AI and machine learning systems. You can check out the Live Link here: https://lnkd.in/geAXUtHC . I would love to hear how others are handling asynchronous AI tasks in production!
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Cover image for Ever watched an AI write
Ever watched an AI write bad SQL, instantly realize its mistake, and rewrite it perfectly—all without human intervention? Over the weekend, I built the Autonomous Data Analyst Agent. It doesn’t just translate natural language to SQL; it actively debugs itself. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Enterprises want AI to query their proprietary databases, but LLMs often hallucinate columns or mess up syntax. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: I engineered a Self-Correcting Reflection Loop using LangGraph. Here’s what happens under the hood: 1️⃣ You ask a complex question in plain English. 2️⃣ The LLM generates DuckDB-flavored SQL. 3️⃣ The Executor node runs it securely in a local container. 4️⃣ 🔄 𝗧𝗵𝗲 𝗠𝗮𝗴𝗶𝗰: If DuckDB throws a parser error, the Reflection node catches it, feeds the error trace back to the LLM, and auto-corrects the query. 5️⃣ Once successful, a Python agent generates a Matplotlib visualization, streaming everything to a sleek Next.js dark-mode UI. Plus, I integrated LangGraph MemorySaver, allowing the agent to retain context for human-like follow-up questions. 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Next.js, FastAPI, LangGraph, DuckDB, Groq (Llama-3), Docker Compose. Everything is fully containerized and open-source. Drop a ⭐ on the repo and let me know what you think! 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼: https://lnkd.in/ddnE9rgq (https://lnkd.in/ddnE9rgq)hashtag#AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) hashtag#LangGraph (https://www.linkedin.com/search/results/all/?keywords=%23langgraph&origin=HASH_TAG_FROM_FEED) hashtag#DataEngineering (https://www.linkedin.com/search/results/all/?keywords=%23dataengineering&origin=HASH_TAG_FROM_FEED) hashtag#Nextjs (https://www.linkedin.com/search/results/all/?keywords=%23nextjs&origin=HASH_TAG_FROM_FEED) hashtag#FastAPI (https://www.linkedin.com/search/results/all/?keywords=%23fastapi&origin=HASH_TAG_FROM_FEED) hashtag#Docker (https://www.linkedin.com/search/results/all/?keywords=%23docker&origin=HASH_TAG_FROM_FEED) hashtag#MachineLearning (https://www.linkedin.com/search/results/all/?keywords=%23machinelearning&origin=HASH_TAG_FROM_FEED) hashtag#OpenSource (https://www.linkedin.com/search/results/all/?keywords=%23opensource&origin=HASH_TAG_FROM_FEED)
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FastAPI(1)