Freelance AI Agent Engineers in Lahore

Freelance AI Agent Engineers in LahoreFreelance AI Agent Engineers in Lahore

Projects People

proLahore, Pakistan

AI Integration & Automation Engineer | Full-Stack Web Apps

$50k+: Earned

64x: Hired

4.9: Rating

115: Followers

Expert

Expert

+2

AI Integration & Automation Engineer | Full-Stack Web Apps

Cover image for Autonomous Multi-Agent Market Research System Development

Autonomous Multi-Agent Market Research System Development

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10

Cover image for Magnai | UK Public Affairs

Magnai | UK Public Affairs

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51

Cover image for Humoni - secure housing in under 72 hours

Humoni - secure housing in under 72 hours

4

101

Cover image for Wellbeing Wizard AI

Wellbeing Wizard AI

8

171

AI Agent Engineer(2)

Lahore, Pakistan

Full Stack Developer - Artificial Intelligence -

Full Stack Developer - Artificial Intelligence -

Cover image for Voice-to-Voice AI Receptionist Assistant for

Voice-to-Voice AI Receptionist Assistant for Businesses My role. AI Agent (Voice-to-Voice AI bot Developer) Project description. Stella AI app, which is a voice-to-voice solution for businesses to manage orders, reservations, and FAQs. I walked through the app's features, including SMS marketing, 24/7 availability, and the ability to customize greeting messages and voice selections. We also explored how to set up a business profile and input a knowledge base and menu through JSON uploads. I demonstrated a live call interaction with Stella AI, showcasing its capabilities in booking appointments. I would love to hear your thoughts on the system and any feedback you might have! Skills and deliverables AI Agent Development AI Bot Artificial Intelligence Natural Language Processing Python

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Cover image for AI based Session Replay Analysis

AI based Session Replay Analysis Tool Built on PostHog My role. Full-Stack Developer / AI Developer Project description. I walk you through the Autoplay app, a session replay analysis tool built on PostHog. We leverage AI to analyze thousands of sessions each week, providing session summaries, user flows, and key insights. I also demonstrate features like our chatbot for communication and the golden path analysis for user flows. I encourage you to explore the filtering options and the recommended issues generated weekly. Your feedback and engagement with the app would be greatly appreciated! Skills and deliverables Session Recording AI Development Full-Stack Development Python Large Language Model

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Cover image for Autoplay AI is a tool

Autoplay AI is a tool I created to help product teams see exactly how users interact with their software. It uses AI to watch how people navigate the product, automatically spotting where they struggle or feel confused. The system then explains why these problems happen and offers clear advice. This helps teams confidently improve their product's design and usability.

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Cover image for AI Voice Agent for Car

AI Voice Agent for Car Dealerships (Manages Inbound phone calls) My role. AI Voice Agent Expert (Voice-to-Voice AI bot) Developer Project description. I demonstrate Dealer Voice, a voice-to-voice agent designed specifically for car dealerships. The system automates call handling, providing summaries and call recordings, and allows for agent management and voice selection. I walk you through creating an agent for a dealership, including setting up the phone number, operating hours, and context for the calls. I also show a live call example where I inquire about available cars, highlighting the system's efficiency. Please test the bot by calling the assigned dealership number to experience its capabilities firsthand. Skills and deliverables Bot Development Natural Language Processing AI Bot Artificial Intelligence Python

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AI Agent Engineer(3)

proLahore, Pakistan

Full Stack Web & Mobile Dev | Next Js | AI Agent | React

1x: Hired

5.0: Rating

26: Followers

Full Stack Web & Mobile Dev | Next Js | AI Agent | React

Cover image for AI-Powered Automated Tech News Blogger

AI-Powered Automated Tech News Blogger

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24

Cover image for Northward Capital Investment Platform Development

Northward Capital Investment Platform Development

2

8

Cover image for Chat Buddy – Real-Time Mobile Chat Application Development

Chat Buddy – Real-Time Mobile Chat Application Development

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6

Cover image for Prodigi Studios

Prodigi Studios

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5

AI Agent Engineer(1)

Lahore, Pakistan

Sr.Full Stack Consultant and Developer with 8+ years of exp

$10k+: Earned

1x: Hired

5.0: Rating

12: Followers

Sr.Full Stack Consultant and Developer with 8+ years of exp

AI-Driven Brand Intelligence Platform Integration

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10

Cover image for Exploring structured prompting with Kittl.
For

Exploring structured prompting with Kittl. For this entry, I created a cinematic luxury footwear concept that captures the moment just before completion, where each component comes together to form the final design. The project includes a women’s high heel video and a three-image sneaker composition, focusing on detail, craftsmanship, and premium aesthetics through realistic textures and refined branding. This highlights how structured prompting can transform an idea into a visually polished result.

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Cover image for 🔥 Full-Stack Development - RankBee

🔥 Full-Stack Development - RankBee AI I worked as a Full-Stack Developer on RankBee an AI visibility platform that helps brands track and improve how they appear across AI search engines. 💡 Key contributions: ⚡ Interactive dashboards for AI visibility ⚡ Tools to track competitors and benchmark presence ⚡ AI-driven content optimization workflows ⚡ Prompt-based testing systems ⚡ Streamlined data processing into actionable insights The platform shows how AI interprets a brand which competitors are recommended and how to optimize content for better AI visibility. I focus on solving complex technical challenges and delivering products that give businesses a real advantage. If you’re developing AI-driven tools or ambitious platforms, let’s collaborate.

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Cover image for I also designed and developed

I also designed and developed https://infinite-compute.com/, a cloud-powered platform that enables users to access high-performance GPU machines and run demanding 3D tools directly from the web without expensive local hardware. I built the complete system architecture myself including the frontend backend services and cloud infrastructure focusing on performance reliability and secure access to remote computing resources. The platform provides a responsive interface to launch and manage GPU sessions while the backend handles session orchestration user management and compute allocation. The system was designed to maintain stable remote sessions efficiently manage resources and provide secure authentication so developers designers and 3D artists can run heavy software on demand from anywhere.

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128

AI Agent Engineer(1)

Lahore, Pakistan

Full Stack Developer | AI & Automation

9: Followers

Full Stack Developer | AI & Automation

Cover image for VeoChain is an AI-powered video generation tool that overcom...

VeoChain is an AI-powered video generation tool that overcomes the typical 8-second limit of AI video models like Google's Gemini Veo 2.0. It intelligently chains multiple video segments together by analyzing each segment's ending and generating continuation prompts, creating seamless 24+ second videos from a single prompt. The app features browser-based video stitching using FFmpeg.wasm (no server uploads needed), a clean Attio-inspired UI built with Next.js and Framer Motion, and persistent project history with IndexedDB. Users simply enter their Gemini API key, write a prompt, and VeoChain handles the multi-segment generation, chaining, and final export automatically.

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Cover image for Kaizen is a Python learning

Kaizen is a Python learning platform for kids ages 12-14 — that specific age window matters because most coding-for-kids products target either much younger (Scratch/blocks) or much older (Codecademy-style courses for adults). The 12-14 zone is underserved: they've outgrown drag-and-drop blocks but they're not ready for "Watch this 2-hour video and take notes." The product is built around a narrative: the digital realm of Coderia is being corrupted, and the kid's job is to save it by writing real Python. Eight story realms (Echo Valley → Logic Peaks → Loop Lakes → ...), 94 lessons, one connected arc. Every lesson is real Python that runs, not blocks.

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Cover image for I designed and built a

I designed and built a full-stack AI healthcare platform from the ground up integrating 6 external medical APIs, real-time AI-powered consultations and a production-grade frontend with smooth animations. This is the kind of intelligent and scalable system I can build for you in healthcare, education or any data-driven domain. Live Demo: healix.vercel.app (http://healix.vercel.app) What Healix Does 4.6 billion people worldwide lack access to essential health services. Healix bridges that gap by putting an AI health companion in every citizen's pocket backed by trusted data from WHO, NIH and the FDA. It delivers 24/7 health guidance, reduces unnecessary ER visits and extends the reach of overstretched healthcare systems. Core Features I Built AI Health Chat - 24/7 intelligent consultations with multi-language support powered by Google Gemini Symptom Checker - Interactive symptom analysis with triage recommendations using NIH Clinical Tables Drug Interaction Checker - Cross-checks multiple medications for dangerous interactions via OpenFDA Medicine Search - Detailed drug profiles including side effects and usage guidelines from OpenFDA and RxNorm Health Dashboard - Personalized health overview with activity tracking and chronic disease management Additional modules include mental wellness tracking, nutrition logging, lab results management, vaccination records, appointment booking and a community health forum. Tech Stack Next.js 15 and React 19 frontend styled with TailwindCSS and animated with Framer Motion. Python FastAPI backend powered by Google Gemini AI with fallback handling. Integrated with OpenFDA, NIH Clinical Tables, PubMed, RxNorm and WHO data sources. Supabase authentication with Google OAuth. SQLite for development and PostgreSQL for production. Deployed on Vercel. What This Demonstrates Complex multi-API orchestration with error handling and fallback logic AI integration with prompt engineering for accurate medical responses Clean component architecture with 30+ routes and reusable UI components Full authentication flow with role-based access Responsive design optimized for mobile-first healthcare access Production deployment with real users in mind Looking to build an AI-powered platform for your industry? Whether it's healthcare, education, finance or operations I can architect and ship intelligent systems like this end to end. Let's talk.

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Cover image for High-Performance RAG Application Development

High-Performance RAG Application Development

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5

AI Agent Engineer(1)

Lahore, Pakistan

AI Developer | RAG Chatbots, AI Agents & Next.js SaaS

New to Contra

AI Developer | RAG Chatbots, AI Agents & Next.js SaaS

Cover image for Meet AI is a video

Meet AI is a video conferencing SaaS where AI agents join live meetings as active participants and respond in real time. I built the full application stack. The hard part is the real-time layer. The backend provisions an AI agent the moment a meeting starts and keeps it running for the full call. Speech runs through a transcript pipeline and generates responses through the OpenAI API in near real time, so the agent replies while the conversation is still moving, not minutes later. After the meeting, the backend writes a structured summary through the same API, handled async by Inngest so nothing blocks the live app. The stack is Next.js 15 and React 19 with the Stream Video and Stream Chat SDKs, tRPC and Drizzle ORM on Neon PostgreSQL, and Better Auth across the stack. The platform runs 30 simultaneous live sessions, each with its own active AI agent, with no backend slowdown. What the client gets: live AI agents that take part in real meetings, plus automatic summaries, on a stack built to hold many sessions at once.

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Cover image for Echo is a multi-tenant SaaS

Echo is a multi-tenant SaaS for AI customer support with realtime chat, an AI voice agent, and AI automation. I was the primary developer on the full build, frontend, backend, and the AI layer. The AI is the core. It runs OpenAI, Claude, Gemini, and Grok through one multi-model setup, so a client can switch providers without a rewrite. A RAG pipeline connected to a vector database grounds every answer in the client's own content, so the chatbot never returns generic output. VAPI powers the voice agent, so customers can speak to support on a live call. Each tenant gets its own AI agent built on its own documents. The stack is Next.js 15 and React 19 inside a Turborepo monorepo, with separate apps for the dashboard, the embeddable chat widget, and backend services. Realtime chat runs on Convex. Clerk handles auth. API keys are encrypted per tenant through AWS Secrets Manager, so no two clients share credentials or data. Launch day held 60 live conversations at once with zero dropped sessions. What the client gets: an AI chatbot and voice agent that answer from their own content, work across multiple LLM providers, and stay isolated and secure per tenant.:

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175

Cover image for I designed and built a

I designed and built a Sentry-style error tracking SaaS from scratch as the Next.js developer and AI integration developer behind it, to prove out multi-tenant architecture and AI integration. Admins manage sub-users with per-project access control. Each admin attaches their own OpenAI, Claude, or Gemini key, encrypted with AES-256-GCM, and picks a model per project for AI fix suggestions. I built two SDKs (Node.js and browser, with a React error boundary) plus a CLI that uploads source maps on build. Errors resolve to original source code, not minified output, the same technique Sentry uses.

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Cover image for Nodebase is a workflow automation

Nodebase is a workflow automation SaaS where users build AI workflows visually with no code, backed by a custom execution engine. I built the full product end to end, alone. The visual builder runs on React Flow inside Next.js 15. Users connect trigger nodes, AI nodes, API nodes, and conditional logic into full pipelines. The backend engine uses topological sort to resolve node dependencies before running each step, so nothing fires out of order. Inngest handles background jobs, retries, and scheduled triggers. Webhooks let outside services start a workflow by hitting an endpoint. This is AI automation in practice. OpenAI, Claude, and Gemini run as built-in AI nodes next to Slack, Discord, and Stripe. A user can route data through an LLM, act on the result, and pass it to the next step, all without code. Credentials are encrypted and pulled at runtime, so keys never sit in plain text. Prisma with Neon PostgreSQL handles workflow ownership, execution history, and job logs. tRPC connects frontend and backend with full type safety. What the client gets: a no-code platform where non-technical staff build their own AI workflows, on an engine reliable enough to run them on a schedule without supervision.

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151

AI Agent Engineer(1)

Lahore, Pakistan

AI/ML & Data Solutions Engineer

New to Contra

AI/ML & Data Solutions Engineer

Cover image for Student Medical Chatbot
Built a chatbot

Student Medical Chatbot Built a chatbot to assist MBBS students in navigating medical literature. Leveraged Llama Index and fine-tuned language models to ensure accuracy. Embeddings were stored in OpenSearch, hosted on AWS. The Django backend included secure authentication and session management for a robust user experience.

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Cover image for Prompt Engineering Mini-Academy is a

Prompt Engineering Mini-Academy is a digital learning product built using Kajabi. It helps users learn how to write better AI prompts and use AI tools for daily tasks such as writing, research, summarization, and productivity. The problem it solves is that many people use AI tools without a proper structure, which leads to weak or generic results. This product gives users a clear learning path, practical prompt templates, and workflow examples to improve the quality of their AI outputs. I used Kajabi to create the landing page, email capture form, downloadable prompt resource, product offer, checkout page, and course structure. A sample video is attached to demonstrate the product flow and user experience.

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Cover image for Retail Knowledge Graph
In this project,

Retail Knowledge Graph In this project, we built a semantic knowledge graph tailored to the retail industry. The pipeline involved developing AI agents to transform heterogeneous data into standardized formats. Ontologies were created to represent domain knowledge accurately. Using Gemini models and LangChain, user queries were converted into Cypher queries to retrieve insights from a Neo4j database. We utilized an MCP server for orchestration and LangSmith for secure login and audit trails. This system enhances complex data exploration for non-technical users.

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Cover image for Developed a full-stack language learning

Developed a full-stack language learning application tailored for Luxembourgish, combining speech recognition, natural language understanding, and generative AI. Fine-tuned OpenAI’s Whisper model for accurate Luxembourgish transcription and built a custom text-to-speech (TTS) engine for realistic audio feedback. A RAG-based architecture enables the app to answer user queries contextually, making learning highly interactive. The frontend is built with React, while Flask powers the backend. Designed to deliver an immersive, conversation-driven auditory learning experience.

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115

AI Agent Engineer(2)

Lahore, Pakistan

ML AI | Backend | Computer Vision | GenAI | LLM Agents

New to Contra

ML AI | Backend | Computer Vision | GenAI | LLM Agents

Cover image for French Legal AI Assistant &

French Legal AI Assistant & Agentic RAG System Overview I designed, built, and deployed a specialized Legal AI Assistant for French lawyers using agentic RAG, legal data pipelines, vector search, reranking, open-source LLMs, and citation-grounded answer generation. The system allowed lawyers to ask legal questions and receive answers grounded in French law articles, legal references, and relevant judicial cases. Problem / Challenge Legal data is very different from normal document data. A generic RAG pipeline using fixed-size chunks often breaks legal meaning, misses important context, or retrieves incomplete references. The main challenges were: 🔹 Legal documents had different structures and lengths 🔹 Articles and laws could not be randomly split into fixed-size chunks 🔹 Each answer needed traceable legal references 🔹 Retrieval had to understand legal scope, not just semantic similarity 🔹 The system needed to reduce hallucinations for legal users 🔹 Deployment had to respect privacy and regulatory requirements My Expertise I worked as the Lead AI Engineer / Agentic RAG Developer responsible for the complete system design and implementation. My responsibilities included: 🔹 Legal data pipeline architecture 🔹 Document parsing and preprocessing 🔹 Custom legal chunking strategy 🔹 Vector database design 🔹 Agentic RAG workflow development 🔹 Retrieval optimization and reranking 🔹 Open-source LLM deployment 🔹 Backend API development with FastAPI 🔹 Secure Azure cloud deployment 🔹 Multi-tenant system support French Legal Data Engineering Pipeline I built an automated ETL pipeline to process thousands of French legal documents, articles, and judicial cases. The pipeline handled: 🔹 Raw legal document ingestion 🔹 Text cleaning and normalization 🔹 Legal article extraction 🔹 Section-aware document structuring 🔹 Custom chunk generation 🔹 Metadata extraction for article number, article title, section, source, and reference 🔹 Embedding generation 🔹 Vector database ingestion 🔹 Repeatable updates for future legal data expansion The chunking strategy was designed so legal articles were not cut in the middle or separated from their meaning. Agentic RAG Workflow Instead of using a simple one-step vector search, I built a LangGraph-based agentic RAG workflow. The workflow included: 🔹 User query understanding 🔹 Legal intent detection 🔹 Legal domain and scope identification 🔹 Generation of 2–5 targeted legal search queries 🔹 Retrieval of relevant chunks for each query 🔹 Deduplication of repeated results 🔹 Reranking of retrieved legal evidence 🔹 Source-grounded answer generation This improved tested retrieval accuracy from around 50% to 95%+. Retrieval, Citations & Case Law The retrieval system was designed to make answers transparent and verifiable. I implemented: 🔹 Vector search for semantic legal retrieval 🔹 Reranking to improve relevance 🔹 Metadata-based source traceability 🔹 Citation-backed answer generation 🔹 Article-level legal references 🔹 Typesense-based retrieval for French judicial cases 🔹 Supporting case law returned with legal answers This allowed lawyers to verify the exact legal source behind each generated response. Open-Source LLM & Cloud Deployment I evaluated and deployed open-source LLM infrastructure for private legal AI usage. The deployment included: 🔹 Qwen2.5:14B for French legal reasoning 🔹 Ollama and vLLM for model serving 🔹 Embedding and reranker models on a private Azure GPU VM 🔹 NVIDIA T4 16GB GPU optimization 🔹 Python/FastAPI backend APIs 🔹 Secure Azure deployment in the France region 🔹 Multi-tenant isolated access 🔹 GitHub CI/CD and Linux server management The system was designed for privacy, reliability, and regulatory compliance. Technologies Used 🔹 Python 🔹 FastAPI 🔹 LangChain 🔹 LangGraph 🔹 LangSmith 🔹 Ollama 🔹 vLLM 🔹 Qwen2.5:14B 🔹 ChromaDB 🔹 Typesense 🔹 Vector Databases 🔹 Reranking Models 🔹 Embedding Models 🔹 Azure Cloud 🔹 Linux 🔹 GitHub CI/CD Impact 🔹 Built a production-ready legal AI assistant for lawyers 🔹 Improved retrieval accuracy from ~50% to 95%+ in tested scenarios 🔹 Reduced hallucinations through citation-grounded generation 🔹 Enabled lawyers to verify answers using article and case references 🔹 Created a scalable legal data pipeline for thousands of documents 🔹 Deployed private open-source LLM infrastructure for legal compliance 🔹 Delivered a strong foundation for future legal AI workflows

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Cover image for AI-Powered PDF Data Extraction
My role:

AI-Powered PDF Data Extraction My role: AI Data Processing and Extracton Engineer Organizations often struggle to extract structured and useful information from large volumes of unstructured PDF documents. I developed a flexible AI-powered data extraction solution that allows users to define the specific entities and fields they want to retrieve. The system processes different PDF formats, identifies relevant information, and converts it into structured, usable data. The solution reduces manual document processing, improves retrieval accuracy, and can be adapted to different document types and business requirements. A working demo link is attached.

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Cover image for AI Agents & RAG Chatbots

AI Agents & RAG Chatbots with Persistent Memory I design and build intelligent AI agents and chatbots that maintain conversation context, retrieve reliable information, and interact with external tools and APIs. Core Capabilities 🔹 Persistent conversation and long-term memory 🔹 RAG-powered answers with reduced hallucinations 🔹 Tool calling, APIs, web search, and file retrieval 🔹 Multi-agent and multi-step workflows 🔹 Integration with OpenAI, Claude, Gemini, and open-source LLMs 🔹 Vector databases including pgvector, Pinecone, Weaviate, FAISS, and ChromaDB Technologies LangGraph, LangChain, Agno, PydanticAI, Haystack, FastAPI, OpenAI, Claude, Gemini, Hugging Face, PostgreSQL, pgvector, Pinecone, and Weaviate

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Cover image for AI Vision for Retail, Industrial

AI Vision for Retail, Industrial & Monitoring Workflows Overview I have built and deployed multiple real-world computer vision systems for industrial inspection, retail automation, and monitoring workflows. My responsibilities covered: 🔹 Dataset preparation and labeling 🔹 Object detection model training 🔹 Segmentation model training 🔹 YOLO-based detection and tracking 🔹 Image/video inference pipeline development 🔹 Model evaluation and threshold tuning 🔹 Production deployment support 🔹 Cloud server management and optimization 🔹 Building practical AI workflows for real-world operational environments Fish Quality Inspection System - lythium.cl (http://lythium.cl) I led the development of an advanced fish quality inspection solution for an industrial workflow. The system used image analysis to monitor fish quality and support automated fish sorting based on AI predictions. 🔹 Led the development of an advanced AI-powered fish quality inspection system for an industrial workflow. 🔹 Built an image analysis pipeline to monitor fish quality from production-line images. 🔹 Trained object detection models to identify fish and relevant visual quality indicators. 🔹 Trained segmentation models to support more detailed visual inspection of fish regions. 🔹 Designed the AI workflow to support automated fish sorting based on model predictions. 🔹 Worked on inspection logic that could classify or route fish based on quality-related outputs. 🔹 Designed the system for conveyor-belt usage, where images need to be processed consistently and reliably. 🔹 Focused on production issues such as image quality, camera consistency, lighting variation, and model reliability. 🔹 Helped convert visual inspection from a manual/rule-based workflow into an AI-supported inspection pipeline. 🔹 Built the system to reduce manual inspection effort and improve production workflow efficiency. Shelfr.ai (http://Shelfr.ai) - Retail Automation Platform I developed AI image solutions for retail automation and execution. The system handled large-scale product detection across 10,575+ SKUs, price tag detection, shelf and display type detection, and gap detection for empty shelf spaces. 🔹 Developed large-scale AI image solutions for retail automation and execution. 🔹 Worked on product detection across 10,575+ SKUs, where each SKU represented a unique product. 🔹 Built object detection workflows to identify products from retail shelf images. 🔹 Developed price tag detection to locate and extract price label areas from store images. 🔹 Worked on shelf and display type detection to understand the retail environment layout. 🔹 Built gap detection logic to identify empty shelf spaces and out-of-stock areas. 🔹 Supported computer vision workflows for retail compliance, shelf monitoring, and store execution. 🔹 Worked with high-volume image data and production-level inference requirements. 🔹 Managed high-load production servers on Google Cloud Platform. 🔹 Implemented load balancing and autoscaling to improve system stability under production traffic. 🔹 Focused on scalable AI infrastructure capable of handling real-world retail image workloads. 🔹 Helped create AI systems for inventory visibility, shelf condition monitoring, and retail execution analytics. lake-shield.com (http://lake-shield.com) - USA LAKES - Boat Detection & Inspection System 🔹 Worked on a YOLO-based boat detection, tracking, and monitoring system. 🔹 Labeled datasets for boat detection and inspection model training. 🔹 Prepared image/video data for object detection training workflows. 🔹 Trained YOLO object detection models to detect boats in monitoring footage. 🔹 Built a detection pipeline capable of identifying boats from visual data. 🔹 Worked on boat tracking logic to monitor boat movement across frames. 🔹 Supported inspection and monitoring workflows using computer vision predictions. 🔹 Developed an end-to-end pipeline from labeled data to trained model and inference output. 🔹 Focused on practical model performance in outdoor environments where lighting, distance, angle, and background can vary. 🔹 Helped build a monitoring system that could support automated detection and review instead of fully manual observation. My Responsibilities Across These Projects 🔹 Led AI/computer vision system development 🔹 Designed labeling and dataset preparation workflows 🔹 Trained YOLO/object detection models 🔹 Trained segmentation models where needed 🔹 Built image and video inference pipelines 🔹 Evaluated models using practical production metrics 🔹 Improved model performance through dataset cleanup, retraining, and threshold tuning 🔹 Integrated AI models into backend or operational workflows 🔹 Supported production deployment and infrastructure optimization 🔹 Worked with real-world constraints such as lighting, camera angle, image quality, latency, and false detection rates Technologies Used 🔹 Python 🔹 YOLO / YOLOv8 🔹 Object Detection 🔹 Image Segmentation 🔹 OpenCV 🔹 PyTorch 🔹 FastAPI 🔹 Google Cloud Platform 🔹 Linux Servers 🔹 Load Balancing 🔹 Autoscaling 🔹 Custom Data Labeling Workflows 🔹 Model Training 🔹 Model Evaluation 🔹 Inference Pipeline Development 🔹 Production AI Deployment

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AI Agent Engineer(1)