Hire the Best Variational Autoencoder Specialists

6+ Years of Full-Time Experience as a Software Engineer. Hands-on experience delivering Startup to Enterprise-grade AI and Software Engineering solutions across multiple industries. Specialized in building AI Agents, RAG applications, LLM-powered automation, workflow orchestration, and business process automation. Experienced in Frontend, Backend, APIs, Databases, Vector Databases, Prompt Engineering, and AI application deployment. Helping businesses automate repetitive workflows, build intelligent assistants, enterprise chatbots, internal knowledge systems, and AI-powered productivity tools. Languages: Python, Java, JavaScript, SQL. AI & LLM: OpenAI, Anthropic Claude, Gemini, Llama, Mistral, Hugging Face Transformers, LangChain, LangGraph, MCP (Model Context Protocol), AI Agents, Agentic AI, RAG, Prompt Engineering, Structured Output, Function Calling. Fine-tuning: LoRA, QLoRA, PEFT, Hugging Face, Unsloth, Model Evaluation, Dataset Preparation. Vector Databases: ChromaDB, FAISS, Pinecone, Qdrant, Weaviate. Backend & APIs: FastAPI, REST APIs, WebSockets, Pydantic, PostgreSQL, MongoDB, SQLite, Redis. LLMOps & Deployment: Docker, Git, GitHub, GitHub Copilot, LM Studio, Ollama, vLLM, CI/CD, AWS, GCP, Azure. Evaluation & Observability: LangSmith, Ragas, DeepEval, Promptfoo, Logging, Monitoring. Support: AI solution consulting, enterprise chatbot development, RAG implementation, workflow automation, debugging, production issue fixing, code reviews, project setup, mentoring, and freelance support.

Hi, Let's turn your AI vision into reality! I'm a Top-Rated Plus AI Engineer specializing in enterprise-grade full stack AI solutions, multi-agent systems, and scalable backend architecture. With 4+ years of focused AI development experience and a proven track record at multinational companies, I transform complex business requirements into intelligent, production-ready applications that drive measurable results. 🎯 Core Specializations: Agentic AI & Multi-Agent Systems Expert in building cutting-edge agentic applications using LangGraph, CrewAI, and Model Context Protocol (MCP). I develop custom multi-agent architectures tailored to specific business use cases, enabling intelligent automation and decision-making systems. Advanced RAG Applications Built 10+ production RAG systems with self-RAG and adaptive architectures. Specialized in implementing evaluation frameworks and optimizing retrieval accuracy for enterprise-scale applications across multiple industries. LLM Fine-tuning & Cost Optimization Proven expertise in instruction fine-tuning (GPT-4o mini), custom model training, and cost-effective model replacement strategies. Successfully reduced client AI costs by up to 40% while maintaining performance through strategic open-source model implementation. Enterprise Backend Development Master-level proficiency in Python (FastAPI, Flask), database design with Alembic versioning, and cloud deployment across AWS, Azure, and GCP. Built scalable systems handling enterprise-level workloads with robust CI/CD pipelines. 💡 Technical Arsenal: AI/ML Frameworks: LangChain, LlamaIndex, Ollama, TGI, VLLM Vector Databases: Weaviate, Pinecone, FAISS, ChromaDB Data Processing: PDF, DOCX, PPTX, Excel, images (OCR), web scraping Models: OpenAI, Claude, Gemini, fine-tuned open-source models MLOps: Docker, CI/CD pipelines, Airflow, model versioning, evaluation frameworks Computer Vision & NLP: Custom model training, Stable Diffusion, OCR optimization Backend: PostgreSQL, MySQL, MongoDB, database versioning with Alembic Cloud & DevOps: AWS, Azure, GCP, containerization, Scalable deployment ✅ Why Choose Me: Proven Excellence: Top-rated freelancer with enterprise-level project experience and multinational company background and startup companies Innovation-Driven: Stay ahead of AI trends, implementing cutting-edge technologies like MCP, adaptive RAG, and latest LLM advancements daily Cost-Conscious Solutions: Specialize in building high-performance AI systems that optimize costs without compromising quality Full-Stack Capability: While AI/ML focused, I possess frontend integration skills and can deliver complete end-to-end solutions Exceptional Collaboration: High availability, flexible timing, proficient with GitHub/GitLab workflows, and always eager to explore new technologies Quality Standards: Write production-grade code, implement proper testing frameworks, and follow industry best practices for maintainable and scalable solution.

I am not just an AI Engineer; I am a storyteller who connects the dots between complex data and business growth. With 5 years of hands-on experience and a robust academic foundation in Statistics and Engineering, I specialize in building AI systems that don't just work they innovate. Why work with me? I don’t just deliver code; I translate your high-level business needs into high-performing, production-ready AI systems that solve real-world bottlenecks. My Core Expertise: - AI Solutions: Text analysis & image recognition - AI Search: Smarter answers with RAG & advanced prompt design - Custom AI Models: Tailored GPT, Gemini, LLaMA, Claude & more - Vibe Coding: Cursor, Lovable, Antigravity, etc.. - AI Workflows: Multi-agent automation for complex tasks - Voice AI: Text-to-speech & speech-to-text (AWS, Google, Azure) - AI Visuals: From idea to image using DALL·E, Midjourney, Stable Diffusion - Automation: Zapier, Make, n8n & custom workflows - Smart Pipelines: Event-driven triggers, error handling & smooth operations AI Agents & Chatbots: I build sophisticated multi-agent and RAG frameworks. Examples include E-commerce virtual associates that drive sales and POS customer support agents that handle complex queries autonomously. Text-to-SQL & Analytics: I enable non-technical users to "talk to their data," providing instant, natural-language insights into sales, inventory, and KPIs. Intelligent Automation (n8n): I streamline operations by eliminating repetitive tasks. My AI-powered HR Agent workflow automatically parses, scores, and ranks candidates to find your "best fit" instantly. Computer Vision & OCR: Expert in YOLO and Qwen2.5-VL. I automate data entry from handwritten or digital invoices directly into structured JSON for accounting and inventory software. Full-Stack AI Deployment: I take models from notebooks to production. Expert in the full AI lifecycle, including MLOps, containerization (Docker), and scalable cloud deployment on GCP. The Toolbox: Frameworks: PyTorch, Keras, TensorFlow, Scikit-learn, OpenCV. LLM Ops & Orchestration: LangChain, LangFlow, DSPy, OpenAI API, Apple MLX. Deployment: Docker, GCP, MLOps pipelines. I am dedicated to delivering results that exceed expectations always on time and within budget. Let’s build your success story. Click the 'Invite' button to start a conversation!

Experienced data scientist with over 4 years of experience in machine learning projects. Active participant on Kaggle, a google backed platform for data science competitions, rated within Top 1% data scientists globally. Have experienced in deep neural networks, convolution neural networks, recurrent neural networks, genetic algorithms, natural language processing, support vector machines, and generative adversarial networks. 𝐒 𝐊 𝐈 𝐋 𝐋 𝐒 ------------------------- 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 : - CNN, - DNN, - RNN/LSTM, - GAN, - Transformers, - Auto-Encoders, - HMM-GMM - SVM, - Boosting, - Random forest, 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 : - C, - C++, - Python, - R, - Matlab, - CUDA(Beginner). 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸/𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 : - Tensorflow, - Tensorflow lite, - Tensorflow js, - Keras, - Pytorch, - RASA, - Pandas, - Scikit-learn, - Open CV, - Tesseract OCR, - Kaldi, - OpenFace, - SpaCy, - NLTK, - Open Pose, - Others as well based on experience. 𝗧𝗼𝗼𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 : - Jupyter-notebook - Google Colab - Pycharm - R-Studio 𝗣𝗮𝘀𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘄𝗼𝗿𝗸 : - Generating karyotype from Chromosome micro slide image. - Identify boundaries of wound and measurement of wound - Extensive object detection, Detection of Sky, Window, Glass, Tree, building. - automated tool for detecting norms and stereotypes in popular culture. - Deep Learning with Image processing for the transformation of style from one image to another. - Mobile embedded object detection model with TF-lite. - Developed an algorithm to generate a 3D model of faces from a single 2D mobile selfie using Python, Convolutional Neural Networks, PyTorch, and CUDA. - Created an algorithm for image and video compression using similarity between images with the help of OpenCV. - Developed an algorithm for the classification of different sounds of drones using MFCC and LPCC features and then SVM and HMM-GMM classifiers. - Created a default loan predictor algorithm with 99.4% accuracy using Python and deep neural networks. - Created an optical character recognition for English language using Python and OpenCV,. - Extracted the Legos from the videos and the classify it into 52 different classes using the convolutional neural network using Python. - Worked on the CNN based binary text classification for the movie reviews to identify the positive and negative reviews with neural networks and Python. - Developed an algorithm for moving object detection, which can find a moving object in a vibrant environment using deep neural networks, OpenCV, and Python. - Many other projects I have worked on. Few of them are signed with NDA. I shall be glad to provide any information/clarification that you might need to make a better decision. If you like my work. Please Direct Message me OR invite me to your job. I will happy to connect and take your project to success. Thanks for taking the time to read through my profile. Looking forward to talking to you. Thanks

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!!

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.