Hire the Best Model Tuning Specialists

I am a Top-Rated AI Engineer specializing in Deep Learning, Computer Vision, and Generative AI with over 7 years of experience and 100+ successfully delivered projects globally. I don't just train models; I engineer end-to-end AI systems that perform flawlessly, scale seamlessly, and maintain ultra-low latency in high-traffic production environments moving your code out of Jupyter Notebooks and into enterprise-grade realities. 🛠️ Core Expertise: •Computer Vision: High-speed object detection/tracking (YOLO v8/v10/v11), multi-camera tracking, face recognition, human pose estimation, and industrial anomaly detection. • Generative AI & NLP: Production deployment of GPT-4/LLMs, custom LangChain workflows, conversational agents, and semantic search. • AI Engineering & MLOps: Model optimization (Quantization/Pruning) for edge devices (NVIDIA Jetson, TFLite, TensorRT) and scalable cloud deployment (Docker, Kubernetes, AWS, GCP). • Document Intelligence: Advanced OCR, License Plate Recognition (LPR), and structured data extraction pipelines. 💻 Technical Ecosystem: Frameworks: PyTorch, TensorFlow, OpenCV, LangChain Deployment/Edge: ONNX, TensorRT, Docker, NVIDIA Jetson, TFLite Cloud & DevOps: AWS (SageMaker/Lambda), GCP, Azure, CI/CD for ML Languages: Python (Expert), C++, SQL 🧪 Proven Production Impact: 🏥 MedTech AI: Built a medical imaging pipeline utilizing deep CNNs/Transformers for real-time diagnostics across 50+ clinical endpoints. 📦 Smart Logistics: Engineered an end-to-end multi-camera tracking system processing 100K+ assets daily with 99.4% accuracy. 🔥 Fire Detection AI: Built a sub-second latency safety monitoring system optimized for low-compute edge hardware. 🚗 Smart City OCR: Developed an automated vehicle tracking system capable of processing high-speed traffic streams. 📊 Predictive Analytics: Fused deep learning perception with business logic, reducing reporting overhead by 90%. 🌟 Why Partner With Me? Production-First: I prioritize stability, cost-efficiency, and low latency over "lab-only" metrics. MVP to Scale: I partner with startups to build rapid, robust Proof of Concepts (POCs) and scale them smoothly. Elite Reliability: 100+ projects delivered on time with clean, maintainable, and well-documented code. ✔️ Open to signing NDAs for confidential enterprise or startup projects. ✔️ Additional codebase demos and architecture details available upon request. Let’s turn your vision into a scalable reality. ✨ Click the "Message" button, and let’s discuss how we can put world-class AI to work for your business. 📩

Hello, I’m the founder of StreamTech, with over 11,000 hours across more than 100 AI and machine learning projects since 2016. I bring deep expertise in computer vision and edge AI ranging from object detection, tracking, and OCR to pose estimation, generative image processing, and event detection in sports feeds. I also excel in building intelligent AI agents and LLM driven chatbots that leverage multi turn dialog, RAG enabled memory systems, and API orchestration. My offerings extend beyond AI models and edge deployment. I provide full mobile experience solutions, creating cross platform React Native apps complemented by thoughtful UI/UX design. Whether it's crafting intuitive interfaces, responsive layouts, or seamless animations tailored for both iOS and Android, I ensure that the user experience complements the underlying AI technology. I guide projects end to end, collecting and labeling data, architecting and training models with PyTorch and TensorFlow, and deploying solutions either in the cloud (AWS, GCP) or on edge devices like Jetson Nano, Xavier, and Orin using DeepStream SDK. My engineering stack includes Python, C++, OpenCV, MediaPipe, OpenPose, SMPL, GANs, Stable Diffusion, Docker, and Kubernetes. At StreamTech, our mission has always been to harness cutting edge tech for meaningful business impact. By blending AI innovation with elegant mobile design, I help entrepreneurs and managers accelerate product development. If you're envisioning a mobile solution powered by CV or conversational AI, or need an AI agent interface that shines on mobile, let’s connect and explore how we can craft something exceptional together. Cheers!!

AI / ML / Agentic AI LLM / Langchain / RAG ChatBots AI / Machine Learning & Computer Vision specialist with hands-on experience building, training, and deploying deep learning models for real-world applications. I specialize in YOLO-based object detection/segmentation and image processing, with a strong background in PyTorch, TensorFlow, and OpenCV, Plus modern RAG and agentic AI LLM systems using LangChain and LangGraph. Key Expertise YOLO Object Detection & Segmentation: Custom training/tuning (YOLO v8–v12), dataset prep, evaluation, and edge AI deployment on Jetson Nano. RAG & Agentic AI: Retrieval-augmented generation with LangChain/LangGraph, vector stores (Pinecone, ChromaDB), tool use, memory, and guardrails. Recommendation AI Engine Systems. Model Deployment & Optimization: Packaging for production (REST), quantization/acceleration where applicable; VM-based deployments. Full Pipeline Development: Data collection → labeling → training → testing → Flask/FastAPI REST APIs → monitoring. MLOps & CI/CD: Experiment tracking, versioning, reproducible workflows, and automated delivery with GitHub/GitLab. API Integration & Apps: Secure API integration with third-party services; Flutter front-ends that consume ML APIs. Cloud & Storage: AWS S3 for datasets/artifacts; deployable on cloud VMs or on-prem. Frameworks & Tools PyTorch, TensorFlow/TFLite, OpenCV, scikit-learn, Pandas, NumPy LangChain, LangGraph, RAG, LLM, Pinecone, ChromaDB Flask, FastAPI (REST API deployment) Databases SQL, MySQL, PostgreSQL, Supabase GitHub, GitLab (MLOps/CI-CD) Google Colab / Jupyter Notebook AWS S3, VM-based deployments React & Flutter (client apps & dashboards)

Design and deploy AI pipelines that transform image, video, and audio data into reliable real-world applications, from computer vision analytics and 3D perception to audio (speech, music, sound) analysis and generative AI systems. My work focuses on production AI systems, not research prototypes. I help companies move from early feasibility studies and PoC development to scalable deployments running in real environments. Typical projects include: • computer vision pipelines for detection, tracking, and segmentation • real-time video analytics and edge AI deployments • 3D vision and spatial perception systems • speech processing and audio AI solutions • generative AI pipelines for image, video, and audio Computer Vision Systems Design and development of advanced computer vision pipelines for image and video analysis. Typical solutions include: • object detection and multi-object tracking • semantic and instance segmentation • pose estimation and motion analysis • OCR and document understanding • visual search and recognition systems These systems are used in industries such as manufacturing, sports analytics, retail, healthcare, and security. 3D Vision & Spatial AI Development of AI systems that understand spatial structure and depth. Experience includes: • structure-from-motion (SfM) • photogrammetry pipelines • depth estimation models • NeRF and neural rendering • point cloud processing and 3D reconstruction Applications include robotics, AR/VR, construction analytics, and digital twins. Edge AI & On-Device ML I specialize in deploying ML models on mobile and embedded devices where latency, memory, and power constraints are critical. Typical optimization techniques include: • model quantization and pruning • architecture optimization • real-time inference pipelines • deployment on mobile and embedded hardware Technologies include: TensorRT, TensorFlow Lite, CoreML, ONNX Runtime. Many deployed systems operate with 50–100 ms inference latency depending on hardware. Generative AI for Vision & Video Development of generative pipelines for media processing and synthetic data generation. Typical solutions include: • image and video generation pipelines • diffusion-based editing and enhancement • synthetic dataset generation for model training These tools help accelerate AI training and improve model robustness. Audio & Speech AI Development of AI systems for speech processing, audio analysis, and voice technologies. Examples include: • phoneme segmentation and pronunciation analysis • speech recognition pipelines • voice feature extraction and audio analytics • generative audio and music models These systems are used in: • language learning platforms • speech therapy tools • voice biometrics systems • music AI applications Technical Stack Frameworks & Models PyTorch, TensorFlow, OpenCV, Detectron2, MediaPipe, YOLO, DINO, SAM, CLIP Deployment TensorRT, TensorFlow Lite, CoreML, Docker, ONNX Runtime, FastAPI Programming Python, C, C++ 3D Vision NeRF, SLAM, Dust3r, point clouds Leadership & R&D I lead an R&D-focused AI team at It-Jim, an AI consulting company with 30+ engineers and 10+ PhDs specializing in: • Computer Vision • Generative AI • Audio & Speech AI • Edge AI systems We help companies solve technically challenging AI problems and build reliable production systems. If you are looking for experienced AI engineers to design, prototype, or deploy advanced machine learning solutions: feel free to reach out!

I am a Machine Learning Engineer with four years of experience working across deep learning research, large scale AI systems, and production model deployment. Over the years, I have worked extensively in medical imaging, computer vision, NLP, signal processing, and large language models, building systems that range from experimental research pipelines to deployed real world AI applications. My day to day work primarily involves Python, PyTorch, TensorFlow, Keras, HuggingFace Transformers, sentence transformers, scikit learn, OpenCV, Pandas, and NumPy. I enjoy working deeply on both the research and engineering sides of machine learning, especially problems that require understanding model behavior rather than simply applying existing architectures blindly. A large part of my background is research driven. I have authored multiple peer reviewed publications in indexed journals and IEEE conferences, including publications in Neurocomputing, Healthcare Analytics, Engineering Applications of Artificial Intelligence, Telematics and Informatics Reports, and other Elsevier and IEEE venues. My research has focused heavily on explainable AI, healthcare AI, and advanced deep learning systems. Some of my published work includes CARDxnosis, an explainable knowledge driven framework for ECG diagnosis and clinical report generation, an explainable AI system for trustworthy arrhythmia detection, a CNN RNN Attention hybrid architecture for automatic modulation classification, ensemble deep learning approaches for lung cancer detection from CT scans, and SRGAN based white blood cell image generation and classification pipelines. Alongside published work, I am currently involved in research on brain tumor segmentation, ADHD and ASD classification from brain connectome graphs, epileptic seizure prediction from EEG signals, and interpretable tabular learning using graph neural networks combined with Kolmogorov Arnold Networks. Beyond research, I have substantial hands on experience building and deploying production grade AI systems. One of my major recent projects was LaborBERT v4, a domain adaptive transformer fine tuning system processing hundreds of thousands of records through a large scale training pipeline. The project involved multiple experimental setups including contrastive learning, masked language model pretraining, temporal contrastive learning, cross attention based fusion, multi task training, and Matryoshka Representation Learning. I have also built hybrid embeddings plus LLM systems for taxonomy mapping using OpenAI embeddings alongside locally hosted LLaMA and Mistral models through Ollama. In addition, I have worked on deployed clinical AI systems and a portable on device diagnostic AI solution with embedded deep learning models for point of care inference, which gave me valuable experience in optimization, deployment constraints, inference design, and production reliability. My broader project portfolio includes vehicle detection using Mask R CNN, human activity recognition on the Kinetics 700 dataset, facial keypoint detection with MultiRes UNet, semantic segmentation pipeline redesign, Stable Diffusion based image editing workflows, toxic comment classification, RASA based conversational AI systems, and large scale scraping and automation pipelines using Playwright and Selenium. I have also worked with Flask and Django based deployment pipelines and cloud hosted ML systems. From an engineering perspective, I care strongly about clean and maintainable systems. I follow disciplined workflows involving modular code design, Git based version control, reproducible experimentation, structured evaluation, bootstrap validated metrics, and detailed documentation. I am also comfortable preparing scientific reports, research papers, and journal submissions using both LaTeX and Word. What ties all of this together is that I genuinely enjoy solving difficult technical problems, especially the kind that require balancing research depth with practical engineering constraints. I am most motivated by projects where thoughtful experimentation, careful system design, and real world usability matter equally.

I’ve helped 50+ clients deliver quality products 3× faster while saving up to $10K/month in salaries using AI-assisted coding powered by solid computer science and machine learning fundamentals. I am a machine learning enthusiast interested in turning your meaningless data into actionable information. With 11 years of software development and 8 years specializing in ML, I’ve built and deployed end-to-end AI solutions across industries, from conversational bots to vision-based grading systems. I also hold a Master’s degree in Machine Learning. Currently, I lead a 45-engineer team delivering production-grade ML products. Our portfolio includes: ✦ Avatare — Conversational AI platform ✦ Compugrade — Revolutionary AI assisted autograding for K-12 ✦ AffectCX — a live conversational bot helping pharma sales teams ✦ Conversational AI Platform — multimodal retrieval + generation system ✦ Card Grading App — computer-vision-based grading for collectibles ✦ Image Steganography (security + CV) ✦ Image Super-Resolution (enhancement using GANs or diffusion) ✦ Urdu OCR (language-specific, low-resource script) ✦ Conversation Detection (speaker recognition / dialogue analytics) ✦ Generative Text/Code Assistant, Multimodal App, Edge-AI deployment 🧠 Technology Stack: Python (full scientific stack) • PyTorch • TensorFlow • Keras • JAX/Flax • ONNX/TFLite/CoreML • OpenCV • Caffe2 • MxNet • Fastai • Ray / Dask / Spark • Docker / Kubernetes • Cloud (AWS/Google/Azure) • Edge / TinyML 🤖 Machine Learning / AI Stack: Generative AI: GPT‑4, Llama 2, DALL·E, Whisper, Claude, Stable Diffusion • Traditional ML: Random Forests, KNN, K-Means, SVM, Logistic Regression • Deep Learning: Neural Networks (ANN), CNNs, RNNs, GANs, Transformers, BERT, Graph-NNs • Modern paradigms: Self-Supervised Learning, Contrastive Learning, Multimodal Learning, Federated Learning • Architecture: Retrieval-Augmented Generation (RAG), Vector-Databases, MLOps (CI/CD, model-monitoring, versioning) 🧩 Problems I’ve Worked On: Image Classification • Object Detection • Semantic / Instance Segmentation • Face Recognition • Neural Style Transfer • Image Super-Resolution • Image Generation (GANs/Diffusion) • Text Classification • Sentiment Analysis • Time-Series Forecasting • Optical Character Recognition (OCR) • Multilingual / Urdu OCR • Information Retrieval • Agronomy Chatbot • Conversational AI Platform • Game Agent with Reinforcement Learning • Pokémon Card Grading App • Image Steganography • Edge/Embedded AI Inference • Multimodal Conversation Detection Above all, I am a quick learner and like to face challenges head-on! Clients who have worked with me like the commitment and the quality of work I do. Feel free to contact me.