Hire the Best PyTorch Professionals

🚀 AI/ML Engineer | Deep Learning | LLMs | FastAPI | Streamlit | RAG Pipelines I’m Aryan Saini, a final-year B.Tech student with 2+ years of hands-on experience building intelligent systems using Python, PyTorch, and modern MLOps tools. I specialize in developing full-stack AI applications—from training custom deep learning models to deploying them via scalable APIs and interactive dashboards. 🔍 What I Can Do for You: • Build and deploy deep learning models (NLP, CV, audio) • Architect RAG pipelines & multi-agent LLM workflows • Integrate HuggingFace, Groq API, OpenAI, and custom fine-tuned models • Develop backend APIs using FastAPI or Express • Create modern UIs with Next.js + Tailwind (Streamlit for quick dashboards) • Deploy on AWS, Vercel, Render, or Docker 🛠️ Key Tools & Frameworks: Python, PyTorch, Transformers, LangChain, FastAPI, Streamlit, Docker, MongoDB, PostgreSQL, OpenAI, Groq, HuggingFace, Tavily, AWS EC2/S3 📌 Highlighted Projects: • ASTRALEARN – AI-powered study planner with LLM agents, RAG search, PDF chatbot • VoxAI – Voice cloning & speech synthesis with TTS/VC models (StyleTTS2, SeedVC) • Multimodal Sentiment Analyzer – Video-based emotion recognition system • Sign Language Recognizer – Real-time gesture classification using CNN + LSTM ✅ Why Hire Me? I’m proactive, fast with iterations, and can take your idea from concept to a fully working AI product. Whether you need help with a proof of concept, research prototype, or a production-grade app, I bring strong technical depth and clean execution. Let’s collaborate to turn your AI vision into reality!

Most AI freelancers build demos. I build systems that ship. 🚀 I'm a Full-Stack AI Engineer with a B.Sc. in Artificial Intelligence and I specialize in one thing: turning complex AI problems into production-ready solutions that scale. From day one, I own the full stack: data ingestion, model architecture, evaluation, API development, containerization, and deployment. No handoffs. No gaps. Just a complete system, built right. 🎯 What I engineer: 🤖 ML & Deep Learning custom model training, fine-tuning, forecasting, classification (TensorFlow, PyTorch) 🧠 LLMs & NLP RAG pipelines, semantic search, document intelligence, agentic systems (LangChain, OpenAI API) 👁️ Computer Vision & OCR detection, segmentation, document parsing at scale ⚡ Production AI APIs high-performance FastAPI/Flask backends, Dockerized and deployment-ready 🔄 Intelligent Automation AI-driven decision systems built for real business logic 🛠️ Stack: Python · PyTorch · TensorFlow · LangChain · OpenAI API · FastAPI · Docker · HuggingFace · OpenCV I don't do experiments. I build things that work reliably, efficiently, and exactly to spec. ✅ If your project demands serious AI engineering, I'm the engineer you're looking for. 💼

£20M+ fundraised. 30K+ users. 80K+ downloads. 4.5K GitHub stars. 3 revenue-generating AI SaaS products. 100% Job Success on every Upwork contract. Top 2% on Upwork. I'm the AI full-stack developer you hire when your project needs to actually work in production. So if you’ve got an idea, let’s bring it to life and make it something that actually works and scales! "Highly recommend working with Okafor. Very responsive and knowledgeable, and I'll be working with him on future projects." Most AI freelancers can build a chatbot. I built the systems underneath: the RAG pipeline that retrieves the right context in under 200ms, the AI agent workflow that reasons across five tools before responding, the voice AI that answers a tenant's call in 600ms flat, and the full-stack SaaS that serves thousands of paying users without falling over. A few things I've built that you can verify: → Felicity (Lightwork AI): LLM-powered voice + chat AI assistant for property management. Hybrid RAG, real-time VoIP at 600ms p95 latency, WhatsApp API integration. The company raised £20M+ and was shortlisted for a UK PropTech Award. I was the founding AI engineer. → FPL Oracle: AI chatbot and sports assistant powered by LangGraph, MCP servers, and GPT. 80% of users rank in the top 10K out of 13million players globally. Currently earning revenue ($1K MRR). → SnapFile: AI document management with OCR, embeddings, and retrieval serving 30K+ users. REST API integrations with Sage, Xero, QuickBooks, Google Drive, and Dropbox. Built with React, Node.js, and PostgreSQL. → DialogFlux: Enterprise multi-tenant communication platform. Omnichannel inbox (WhatsApp, SMS, email, voice), AI-powered sentiment analysis, real-time WebSocket dashboards. Processes millions of messages. Built with Python, TypeScript, and Redis. → Databricks (contract): Built autonomous browser automation agents using LangGraph + Puppeteer for compliance research and web scraping. Improved pricing accuracy by 11%, cut manual research time by 45%. Tracked with MLflow. → Open Source: My machine learning image restoration project earned 4,400+ stars on GitHub, 80K+ downloads, and has been cited in 50+ publications. Top 10 ML project on GitHub in 2022. Built with Python, PyTorch, and TensorFlow. What I build for clients: AI agents and LLM systems: multi-step reasoning with LangChain, LangGraph, MCP, OpenAI, GPT, Claude, Gemini, and Hugging Face. AI chatbot development for web, mobile, and WhatsApp. RAG pipelines with FAISS, Pinecone, and vector databases. Voice AI with Twilio, WebRTC, and real-time streaming. Full-stack development: React, Next.js, Node.js, NestJS, Python, TypeScript, GraphQL and REST APIs. PostgreSQL, MongoDB, Redis. SaaS platforms, dashboards, marketplaces, and multi-tenant apps. Automation and integrations: workflow automation with n8n, Make, and Zapier. Stripe, HubSpot, Notion, Slack, Airtable, and DocuSign integrations. Browser automation and data extraction at scale. DevOps and deployment: Docker, AWS, Vercel, CI/CD pipelines, Terraform, and GitHub Actions. I don't just write code for clients. I run 3 of my own AI products with paying users. That means I think about retention, not just features. Revenue, not just releases. Uptime, not just upvotes. How I work: I respond within an hour. I scope before I start. I ship weekly, not monthly. Docs and post-launch support are standard. I currently take on 2-3 projects at a time to give each one the attention it deserves. MSc in Artificial Intelligence (University of Surrey). Former Research ML Engineer at Databricks. Founding AI Engineer at Lightwork AI. Open source contributor to TensorFlow, PyTorch, and Ivy. Click "Invite" or send me a message with your project details. I'll reply with initial thoughts and a rough scope within an hour.

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 have Expert-Vetted Talent (EVT) badge - it's Upwork's top 1% freelancers — pre-screened by Upwork Talent Managers and experts in their field. Out of ~30 million programmers worldwide, only a few thousand know Algorithms & Data Structures better than I do, which is proven by programming competitions. Please, contact me if you need that skill level (top 0.01%). I can do algorithmic/performance work in C/C++, Python, SQL, Java, MQL4, MQL5, C#, Assembly, JavaScript, Julia, Rust, and probably other languages - learning them rapidly. I also work with AI, mostly in NLP and NLU: large language models including OpenAI GPT-3, Bloom, BloomZ, GPT-J 6B, LLaMA, Alpaca, etc.; HuggingFace Transformers, Accelerate; Petals, Deepspeed, zfp/zfpy; CUDA, CPU, and MPS (AArch 64 M2 MacOS Metal GPU) backends. I have some work experience with Apple Neural Engine (ANE). In AI, I also worked with XGBoost for predictions (including trading), LibSVM, TensorFlow, PyTorch, Scikit-learn, etc. I have the hardware in my home office for training and inference with large language models and other AI. English: C1 (Grammarly plugin says I use more unique words than 95% of other users, native speakers included). Polish: B1 (86%). Russian, Belarusian: Native. - With unique skills in Algorithms & Data Structures, I improve programs asymptotically (often 100 or more times on large input data). - 29 years of programming (started Basic and assembler on ZX Spectrum), 24 years of C/C++, 16 years of commercial work experience + 3 years of research projects. - Contributed to widely used Open Source projects: LLVM/Clang (my contribution is XRay profiler on ARM32 and AArch64 systems), Katana Graph (multiple small contributions mostly driven by the proprietary part where I do GPU/CUDA), CBMC "C Bounds Model Checking" (I contributed parallelized output of DIMACS formatted Boolean Satisfiability formula), oatpp (C++ web framework, I contributed bug-fixes), OWL (OptiX Wrapper Library, I contributed build fixes for Ubuntu), Galois (research project for distributed computations on graphs, I contributed GPU improvements) - Actively participated in bug reporting and reproduction (for NVIDIA CUDA, Cadical&kissat boolean satisfiability solver, JBOSS, MariaDB, Tensorflow, Linux, etc) - Led several open-source projects of my own: ProbQA (a video game recommendation system based on a high-performance Bayesian inference engine with CUDA, SIMD, and multi-threading); InSoAr (automatic reconstruction of software architecture from source code ), a multi-threaded Boolean Satisfiability solver, etc. Working for hire, implemented: - efficient multi-threading, scaling real-world workloads almost linearly with the number of CPU cores (128x for AMD Ryzen Threadripper 3990X) - SIMD vectorization (SSE/AVX), up to 8x improvement in computing thread or even copying (see my "Faster alternatives to memcpy" answer on Stackoverflow, URL upon request). - cache-aware algorithms: up to 50x improvement on some workloads - up to 20 trillion operations/second in CUDA (thousands of times faster than CPU) - up to the theoretical limit (6.8 Gigarays/second on RTX 2080 laptop GPU) in ray-tracing with OWL and OptiX - AVX512 and RTM (Restricted Transactional Memory) based acceleration, 16x improvement for float numbers -up to 20x improvements to cryptocurrency miners on CPU using AVX512 and cache-friendly algorithms - crypto-miners for Ethereum, Bittensor, Qubic 15K reputation on Stackoverflow: (1915854/serge-rogatch) Topcoder SRM score: 1480 - among top 5K programmers in the world - top 0.02% (rSerge) I developed all kinds of networking applications, from Linux Kernel modules up to Web applications. The majority of work was, of course, done at TCP/IP level with socket calls like send/recv/select. Programming languages: C++, C++11/14/17/20, C, Python, x86/x64/ARM/AArch64 assembly, SQL, C# .NET, JavaScript, HTML, CSS, Java, MQL4, MQL5, XML, Cypher, Rust. Libraries/Frameworks: PyTorch, Tensorflow, HuggingFace Transformers/Accelerate/Safetensors, Hivemind/Petals, OpenAI, tiktoken, Django, Flask, STL, LibSVM, XGBoost, libcurl, Selenium, PyTorch, Transformers. Technologies: OpenMP, CUDA, SIMD (AVX&SSE, RTM), Linux Kernel Modules, OptiX/OWL, RTX, raytracing. Theory/Principles/Know-how/Methodologies: Algorithms & Data Structures, Performance Optimization, Artificial Intelligence, Multithreading, Vectorization, OOP, Low-latency, High-frequency, Blockchain. Open source code: Clang, LLVM, LLVM's compiler-rt library, Linux Kernel, a few of my own repositories, contributions to AI and Algorithm open-source projects such as Petals and CBMC. Tools/APIs/Architectures/Platforms: PostgreSQL, MSSQL, MySQL, Neo4j, MATLAB, CMake, GIT, MT4, MetaTrader 5, Conda, PyCharm. Virtual Machines / Containers: Docker, VMWare, VirtualBox, QEmu, Hyper-V. OSes: Windows, Linux, Android, macOS

I build production-grade AI systems that automate workflows, transform data into intelligence, and enable businesses to operate faster using LLMs, RAG, and advanced automation. Most freelancers deliver prototypes — I design and deliver complete, scalable systems (architecture → pipelines → APIs → deployment) ready for real-world use. I specialize in turning unstructured data, documents, and internal knowledge into intelligent AI-powered systems that drive efficiency and decision-making. 𝗖𝗼𝗿𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Custom RAG pipelines with intelligent retrieval and reasoning • Private AI assistants trained on your business data • Semantic search, document Q&A, and knowledge automation • Scalable vector database integrations 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 • OCR → AI extraction → structured outputs • Document classification, summarization, and insights • AI workflows for legal, financial, and operational documents • Systems that significantly reduce manual processing 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 & 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • End-to-end automation pipelines across tools and platforms • AI-driven workflows for support, onboarding, and operations • CRM and API integrations (HubSpot, Zoho, Pipedrive) • Custom backend systems for analytics, reporting, and compliance 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗪𝗼𝗿𝗸 𝗪𝗶𝘁𝗵 𝗠𝗲 • I build systems, not scripts — complete, production-ready solutions • Strong engineering + architecture + product thinking • Experience with complex workflows and multi-step AI systems • Clear communication with structured progress updates • Focus on long-term, scalable solutions 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗙𝗼𝗰𝘂𝘀 LLMs, RAG architectures, vector databases, backend systems, and scalable deployments using modern AI and cloud technologies. If you need an AI engineer who can design, build, and deliver intelligent systems that create real business impact. let’s connect.