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I reduce operational costs by 50-70% through production-grade AI voice agents that handle 1,000+ daily calls with sub-500ms latency. Not prototypes. Not demos. Real systems deployed for 15+ U.S. companies, processing 2,000+ qualified leads monthly, and running 24/7 on 200+ edge devices. Currently Founding Engineer at Phi Consulting, where I architect autonomous voice agents for recruiting, sales, and healthcare workflows. Previously: AI Engineer at Turkish Aerospace (mission-critical systems for 500+ engineers) and Senior AI Engineer at Kodifly (real-time computer vision at 30-60 FPS). WHAT I DELIVER (In 2-4 Weeks) VOICE AI AGENTS THAT ACTUALLY WORK - Inbound/outbound call handling with natural conversation flow - Barge-in and interruption management (sub-300ms response) - Appointment scheduling via API (OpenDental, Cal com, custom PMS) - Call state management: follow-ups, confirmations, missed call recovery - Human escalation pathways that don't break the UX RETELL AI MIGRATION SPECIALIST - Migrated custom voice systems to Retell with functional parity - Fixed stability issues that plague DIY voice AI builds - Webhook architecture for reliable call state persistence - 99.9% uptime through defensive error handling AGENTIC AI AND AUTOMATION - OpenCLAW, MOLTBot, Twilio - Multi-agent systems for lead sourcing (2,000+ qualified leads/month) - MCP (Model Context Protocol) for document parsing and tool use - Deterministic business logic — no AI hallucinations on critical paths - RAG pipelines with 95%+ accuracy for enterprise knowledge retrieval WHY CLIENTS HIRE ME (And Rehire) PRODUCTION-FIRST MINDSET I don't ship MVP experiments. I ship systems that handle real traffic on day one. Circuit breakers, retry logic, monitoring, and graceful degradation built-in. SPEED WITHOUT SACRIFICE 8-10 week MVP delivery with weekly demo checkpoints. Fixed-price milestones so you pay for outcomes, not hours. If I'm wrong on scope, I absorb the cost — not you. BATTLE-TESTED ARCHITECTURE Built voice agents processing 1,000+ calls/day. Built computer vision systems monitoring 100+ camera feeds simultaneously. Built AI for aerospace with zero-failure tolerance. Your project isn't my first rodeo. COMMUNICATION THAT RESPECTS YOUR TIME Daily async updates. Clear Done equals acceptance criteria. No jargon, no surprises, no "it's 90% done" for three weeks. RECENT PROOF (Last 12 Months) Phi Consulting | Voice-First Recruiting Automation - 1,000+ daily calls handled autonomously - 2,000+ qualified leads generated monthly - 60% reduction in client recruiting costs - Sub-500ms average response latency Turkish Aerospace | Enterprise AI Infrastructure - AI systems for 500+ engineers - 10,000+ software licenses managed - Mission-critical HPC with strict audit compliance Kodifly | Real-Time Infrastructure Monitoring - 100+ camera feeds processed simultaneously - 95%+ mAP detection accuracy in Hong Kong tunnels - 30-60 FPS real-time inference TECH STACK I WORK WITH Voice AI: Retell AI, Vocode, Pipecat, Twilio, Deepgram, ElevenLabs, OpenAI Realtime API Backend: Python, FastAPI, Nodejs, Postgres, Redis, Docker AI/ML: LangChain, LangGraph, OpenAI, Anthropic, HuggingFace, TensorRT Infrastructure: AWS, GCP, NVIDIA Jetson (Edge AI), vLLM HOW TO START (Next 48 Hours) Option A: 15-Min Diagnostic Call I'll audit your current workflow and identify the highest-ROI automation opportunity. No pitch, just analysis. Option B: First Milestone ($2,500) Working voice agent handling test calls with your conversation flow, delivered in 5 days. Pay only if it meets acceptance criteria. Option C: Project Roadmap I send a 2-slide technical architecture plan within 24 hours of your message. AVAILABILITY: 30 hours/week for new projects (Q1 2026) Currently booking projects for February-March start dates. Voice AI demand is spiking — message now to reserve capacity. Send me your requirements. Within 24 hours, you'll have a concrete approach. Not a generic proposal — exactly how I'd solve your specific problem.

Most machine learning projects fail between the prototype and production. I've shipped 47+ that didn't. 🎯 YOLO Detection | 🧍 Pose Estimation | 🏋️ Sports AI | 🛒 Retail AI | 🛡️ CCTV Analytics | 🔄 Tracking | 🧠 ML Pipelines | 🤖 AI Agents | 💬 LLM Integration You have a working concept — or a clear problem involving cameras, video, or image data. The challenge is making it fast, accurate, and stable under real-world conditions. Wrong framework choices. Inference too slow for live video. Models that break the moment lighting, angle, or environment changes. And systems that detect things but can't reason about them or act on them autonomously. That's exactly where most builds stall. I design and build real-time computer vision pipelines that go all the way — from model training to live deployment — and increasingly, from visual perception to autonomous AI agents that understand, decide, and narrate. Object detection · Machine learning · Pose estimation · Multi-camera tracking · Segmentation · Re-identification · Anomaly detection · OCR & ANPR · Optical flow · Depth estimation · LLM-powered reasoning · Agentic decision pipelines While most CV engineers stop at training the model, I go further: → Accelerated inference with TensorRT, ONNX, OpenVINO, and FP16/INT8 quantization (up to 5× faster) → LLM agents layered over CV pipelines for real-time decisions, alerts, and natural language outputs → Mobile deployment via CoreML (iOS) and TFLite (Android) with 10+ live apps shipped → Edge deployment on Jetson, OpenVINO, Apple Neural Engine, and CUDA/cuDNN → End-to-end pipeline: camera input → training → optimization → real-time actionable output Key Accomplishments: ⭐ Generated $5M+ in client revenue ⭐ Delivered 100+ end-to-end computer vision systems ⭐ Successfully launched my own 2 SaaS products ⭐ Real-time sports AI for 7+ sports, improving analytics for 15+ teams ⭐ Mobile AI on iOS (Core ML) & Android (TFLite), powering 10+ apps ⭐ Surveillance, safety, and industrial AI solutions ⭐ Medical imaging AI for 5+ hospitals: tumor detection, ultrasound, test strips ⭐ Model optimization: up to 5× faster inference using FP16/INT8, ONNX, TensorRT, OpenVINO ⭐ Multi-object tracking, re-identification, Model Training 1M+ labelled Dataset ⭐ Agentic CV systems that perceive, reason, and act without human input in the loop If you have read this far, please note that I appreciate you taking the time to learn about me. Personally, it’s been an amazing journey and knowledge exercise to get to this level of competence in AI and software development. Domain Expertise: - Sports & Fitness: athlete tracking, shot detection, scoring automation, drill analysis, pose estimation - Industrial & Workplace: tire defect inspection, PPE compliance, staff monitoring, meter reading, machine vision inspection, automated quality control - Surveillance & Security: ANPR, crowd monitoring, people counting, animal attack detection, exam cheating detection, perimeter security, intrusion detection - Healthcare & Medical: tumor detection, ultrasound processing, test strip analysis, X-ray/CT scan processing, lesion segmentation, medical image annotation - Traffic & Transport: aerial monitoring, traffic flow AI, license plate recognition, vehicle detection, accident detection, parking management - Retail & Business: customer analytics, receipt extraction, retail intelligence, object recognition, shelf monitoring, inventory management Tech Stack: Machine Learning, Deep Learning, YOLOv5, YOLOv8 - YOLO26, Detectron2, DeepSORT, StrongSORT, MMDetection, MediaPipe, OpenPose, PoseTrack, Action Recognition, Semantic Segmentation, Instance Segmentation, OCR, Anomaly Detection, Motion Detection, Object Counting, License Plate Recognition, PyTorch, TensorFlow, TensorFlow Lite, Keras, OpenCV, FastAPI, Flask, Core ML, TFLite, ONNX, TensorRT, OpenVINO, CUDA, Swift, Kotlin, Flutter, Python, C++, AWS, GCP, Azure, Edge Deployment, Mobile AI, Real-Time Inference, Surveillance AI, Aerial Drone Analytics, Video Stream Analytics, AI Automation, LLM Integration (GPT-4o, Claude, Gemini, Groq), AI Agent Frameworks (LangChain, LangGraph, CrewAI), RAG Pipelines, Streaming LLM Inference license plate recognition, aerial drone analytics, surveillance AI, mobile AI, embedded systems, deep learning pipelines, inference optimization, video stream analytics, AI automation, AI for industry 4.0, computer vision pipelines. If your project involves cameras, video, or images — and you need it fast, accurate, fully deployed, and intelligent enough to reason and act autonomously — I am the engineer you are looking for.

Most computer vision projects fail not in training — but in deployment. Models that hit 95% accuracy in the lab break down when lighting shifts, hardware stutters, or the camera feed isn't clean. I build systems engineered to survive those conditions — and I've done it across industries, hardware platforms, and deployment environments. I'm a Computer Vision Engineer specializing in end-to-end AI pipelines — from raw camera input to real-time inference, deployed on edge hardware, cloud APIs, or both. ━━ Core services ━━ → Object detection & multi-object tracking — YOLOv8, YOLOv5, ByteTrack, BOTSort, MMDetection → Segmentation, pose estimation & keypoints — MediaPipe, custom model architectures → Edge AI deployment — NVIDIA Jetson Orin/Nano, Raspberry Pi, Hailo — TensorRT, ONNX, INT8/FP16 → Cloud & API deployment — FastAPI, Docker, AWS GPU instances, REST & WebSocket inference APIs → Video analytics & smart camera systems — safety monitoring, defect detection, zone tracking, people counting ━━ Systems I've shipped ━━ ✓ Real-time fall detection on NVIDIA Jetson — production-deployed, sub-100ms latency ✓ Zone-based people tracking & monitoring for safety-critical environments ✓ Industrial defect detection pipeline — TensorRT-optimized, running on constrained edge hardware ✓ End-to-end smart camera system: camera → inference → dashboard & real-time alerts ✓ OpenCV video analytics pipelines with custom pre/post-processing and business logic ━━ What makes my work different ━━ Most CV engineers deliver a model file. I deliver a working system — optimized, integrated, and running reliably in your environment. I lead a small team and personally own system architecture, optimization strategy, and core AI engineering on every project. You get senior-level technical execution, not delegation to juniors. Edge or cloud. Jetson or GPU server. Prototype or production scale. I've built across all of it. ━━ How a typical project runs ━━ 1. Discovery — review your hardware targets, data sources, and latency requirements before any code is written 2. Architecture — design the full pipeline: model selection, optimization path, deployment stack, integration points 3. Build & optimize — iterative development with benchmarked FPS and accuracy metrics at each stage 4. Deployment — containerized, documented, and running on your target environment 5. Handover — clean codebase, inline documentation, and a session so your team can maintain it independently ━━ Full tech stack ━━ Models: YOLOv8, YOLOv5, YOLOv7, MMDetection, Detectron2, PyTorch, TensorFlow, ONNX Runtime Tracking: ByteTrack, BOTSort, DeepSORT, StrongSORT, custom zone logic & counting algorithms Optimization: TensorRT INT8/FP16, ONNX quantization, model pruning, batch inference tuning Edge hardware: NVIDIA Jetson Orin/Nano, Raspberry Pi 4/5, Hailo-8, Coral TPU Cloud & infra: FastAPI, Flask, Docker, AWS EC2/Lambda, GCP, RTSP/RTMP stream processing Vision utilities: OpenCV, FFmpeg, GStreamer, PIL/Pillow, custom pipeline components ━━ Project types I take on ━━ → Greenfield CV systems — full pipeline from scratch to production deployment → Model optimization — take an existing model and make it production-fast on your hardware → Edge porting — migrate a cloud-based CV system to Jetson, Raspberry Pi, or Hailo → Pipeline debugging — diagnose and fix latency, accuracy, or stability issues in live systems → Inference API — wrap your CV model as a scalable, low-latency REST or WebSocket API → PoC → production — take a working demo and harden it for real-world deployment at scale → Team augmentation — embedded senior CV engineer for sprints or longer-term engagements ━━ Industries served ━━ Manufacturing & quality control — defect detection, visual inspection, production line monitoring Safety & security — real-time threat detection, perimeter monitoring, crowd analytics Retail & logistics — shelf analytics, people counting, queue management, warehouse tracking Healthcare — patient monitoring support systems, lab automation, medical imaging pipelines Agriculture — crop health detection, drone-based aerial inspection, field monitoring systems ━━ Common questions ━━ Work with our existing dataset? Yes — I assess quality, recommend augmentation strategies, and fine-tune models on your labeled data. Edge or cloud deployment? Both — Jetson, Raspberry Pi, and Hailo at the edge; AWS GPU instances and containerized APIs in the cloud. Can you take our prototype to production? That's one of my most common engagements — hardening, optimizing, and deploying existing concepts for real-world reliability. Documentation and handover included? Always. Clean code, inline comments, deployment instructions, and a dedicated handover session on every project. If you need computer vision that performs beyond lab conditions — on real hardware, with real data, in real-world environments — let's talk.

I have been building production AI systems for 6 years, and the one thing that has actually changed is speed. I am shipping code 100x faster than I was in 2019, without compromising on quality. Computer Science background, 6 years of hands-on experience building products and startups. If you want someone who treats your product like a business problem to solve, not a ticket to close, you are in the right place. I bring an engineering team behind me, so you get senior-level execution with the bandwidth of an agency when the project needs it. What I build: - AI Agents and Multi-Agent Systems (LangChain, LangGraph, CrewAI) - RAG Pipelines with Pinecone, PGVector, Weaviate - Voice AI Agents (Vapi, Retell, ElevenLabs, Deepgram, Twilio) - AI Chatbots and Conversational AI - LLM Applications (OpenAI, Claude, Llama, Groq, Ollama) - MCP Servers and Custom AI Tooling - Full-Stack AI SaaS Platforms - AI Automation Workflows (n8n, Python pipelines) - Computer Vision and Model Fine-Tuning - AI Deployment on AWS, GCP, Docker Industries I have shipped for: Fintech (Coinbase), Healthcare (DriveHealth), HVAC, Legal and Compliance, Education, SaaS startups, E-commerce. How I work: - Hourly contracts: I do the work myself, end to end. - Fixed-price contracts: I distribute work across my team in Islamabad. I stay personally accountable to you. Team members join calls only when their expertise is needed. Transparency is the rule here. My core stack: Python, LangChain, LangGraph, CrewAI, PyTorch, FastAPI, Next.js, React, PostgreSQL, PGVector, Pinecone, AWS, Docker, GitHub Actions. Why clients work with me: - Production-ready systems, not prototypes - Direct accountability, no project manager middle layer - On-time delivery backed by agile practices - Long-term code quality you can build on Send me a message with your project details. I reply within a few hours during business days and I am always up for a real conversation about cool projects with serious operators.

Data science Expert/Machine Learning/Deep Learning/NLP/COMPUTER VISION. --5+ Years of Professional Experience in Machine Learning -- Specialized in Generative AI, Image Generation, and Speech Generation AI & Generative Model Specialist | 5+ Years of Experience in Building Intelligent Solutions I am an AI specialist with over 5 years of hands‑on experience in machine learning, deep learning, and generative AI, bringing a solid research background and real‑world expertise to deliver cutting‑edge, production‑ready solutions. My work focuses on transforming complex ideas into intelligent, scalable systems that drive real business value. What I Bring to the Table Generative AI Mastery: Advanced expertise in GANs, VAEs, diffusion models for image, video, and speech generation. --LLMs & Multimodal AI: Skilled in designing, fine‑tuning, and integrating large language models with multimodal capabilities (text, image, and speech). --End‑to‑End ML/DL Development: From architecture design and model training to deployment and continuous optimization across platforms. --Diverse AI Applications: Delivered solutions for text‑to‑image, image‑to‑image, speech‑to‑text, text‑to‑speech, and voice cloning, along with content generation, image captioning, visual understanding, and speech synthesis. --Deployment Expertise: Optimizing AI models for cloud, mobile, and edge devices using PyTorch, TensorFlow, and NVIDIA frameworks for speed and efficiency. --Research & Innovation Published research in Reinforcement Learning at ICML (“Inverse Constrained Reinforcement Learning”) with applications in human‑in‑the‑loop generative model improvement. --Expertise in Reinforcement Learning from Human Feedback (RLHF)—a powerful technique used in building next‑gen LLMs and generative models. --Technical Toolbox --Core Areas: Generative AI, Computer Vision, NLP, Speech & Audio Processing, Multimodal AI, OCR, Large Language Models, Big Data Analytics --Frameworks & Libraries: TensorFlow, PyTorch, Hugging Face, Keras, OpenAI, CoreML --Tools & Platforms: FastAPI, Flask, CUDA, OpenCV, TFLite --DevOps & Cloud: AWS, GCP, Kubernetes, Docker, GitHub, GitLab Looking for an AI partner who can design, build, and deploy innovative solutions? Let’s bring your vision to life — whether it’s AI‑powered content creation, smart automation, or custom multimodal solutions, I can deliver results that exceed expectation Thank you for taking the time to review my profile.. I am very passionate about being a competent and contributing team player. I look forward to answering your questions and opportunities. Regards. Maryam khan

I am a low-level systems engineer specializing in Windows Kernel Driver Development, Linux Kernel Modules, C/C++, Rust, firmware, real-time telemetry systems, and performance-critical system components. I help companies build stable, secure, and production-ready low-level software, including kernel drivers, virtual devices, telemetry collectors, hardware-software integration layers, and high-throughput system components. What I can help you build ✔ Windows kernel drivers using WDK, KMDF, and WDM ✔ Linux kernel modules and embedded Linux drivers ✔ Boot-start kernel drivers ✔ Filter drivers and virtual device drivers ✔ Virtual audio, input, network, and monitoring drivers ✔ Real-time telemetry pipelines ✔ Kernel-to-user communication using IOCTL, shared memory, and IPC ✔ WFP and ETW-based system monitoring layers ✔ High-throughput streaming and data pipelines ✔ Security-focused kernel infrastructure for authorized systems ✔ Driver debugging, BSOD analysis, and crash dump investigation ✔ Driver signing, HLK testing, and production deployment support ✔ Driver porting and compatibility updates for new Windows versions Core technical expertise Windows Kernel Development WDK, KMDF, WDM, filter drivers, WFP, ETW, IOCTL design, shared memory, boot-start drivers, driver signing, HLK testing, x64, ARM64. Linux Kernel Development Linux Kernel Modules, character/block drivers, Netfilter, basic eBPF integration, Device Tree, Sysfs, Procfs, kernel synchronization, and IPC mechanisms. Systems Programming C, C++, Rust, firmware-level programming, embedded systems, hardware communication, performance optimization, memory-safe architecture, and low-level debugging. Types of projects I handle Custom Windows kernel drivers Virtual audio/input/network drivers Real-time system monitoring drivers Network filtering and packet inspection Secure telemetry collectors High-speed data pipelines Kernel performance optimization BSOD and crash dump debugging Driver modernization and migration Linux kernel module development Embedded Linux driver work Rust/C++ system-level tools and components I focus on clean architecture, stable implementation, clear communication, and production-ready delivery. If your project requires deep low-level engineering expertise, I can help you design, build, debug, and ship it properly.