Hire the Best Machine Learning Engineers

I'm happy to start with a free consultation, quick POC, or a test task, your call. See the quality first, then decide. I build production AI systems: computer vision pipelines, RAG knowledge bases, LLM fine-tuning, voice and chat agents that run at scale for market giants serving millions of customers. $300K+ earned across 85 Upwork contracts and 4,638 hours, 100% Job Success, Top Rated Plus. I lead the AI engineering team at AB Ark. WHAT I BUILD Computer vision Object detection and tracking (YOLO, OpenCV), CCTV and video analytics, edge inference on NVIDIA Jetson, facial expression and body-language models, OCR and document layout analysis, image segmentation. RAG and knowledge systems Private document brains over Google Drive, SharePoint and internal wikis, with citations back to source. Vector search (pgvector, Pinecone, Qdrant), hybrid retrieval, re-ranking, multi-LLM routing, document classification and extraction. LLM engineering Fine-tuning and LoRA training, prompt architecture, evaluation harnesses so you can measure whether a change helped, structured output and schema enforcement, GPT, Claude and open-weight model integration. Voice and conversational AI Real-time voice agents on Twilio, Telnyx, Retell and LiveKit including human-like interruption handling and warm transfer to a live agent. AI agents and automation LangChain and LangGraph agents with tool calling, multi-step workflows, retrieval and human-in-the-loop approval steps. Deployment and MLOps Docker, Kubernetes, CI/CD, AWS and GCP, model serving, monitoring and drift detection. FastAPI and Django when the model needs an API around it. RECENT WORK Edge video analytics on NVIDIA Jetson: real-time object detection on CCTV streams for an on-premise deployment Private RAG "Knowledge Brain" over Google Drive with SOP indexing and citation-backed answers Computer vision SaaS for CCTV footage analysis, built as a multi-tenant product Telnyx voice assistant with warm transfer and human-like interruption handling LLM/RAG document classification and workflow design Computer vision models for facial expression, body-language analysis or custom object detection STACK Python · PyTorch · TensorFlow · OpenCV · YOLO · Hugging Face Transformers · spaCy · scikit-learn · LangChain · LangGraph · LlamaIndex · OpenAI · Anthropic · pgvector · Pinecone · FastAPI · Django · PostgreSQL · Docker · Kubernetes · AWS · GCP · NVIDIA Jetson HOW I WORK Discovery first. I define the data, the model approach and the evaluation metric before writing training code, so "done" is measurable rather than argued about. Milestones with written acceptance criteria, or hourly with daily updates. Your choice. You own the code, the model weights and the infrastructure. NDA and IP assignment on request. Send me your dataset, your accuracy target, or the pipeline you have now, and I will come back with an approach, the risks, and an estimate.

Hello, I'm Tam 👋 - 7+ years of experience in Deep Learning, Computer Vision, LLM, and Generative AI. - 3+ years of experience in AI Automation, RAG, AI Agents. - Tech stack: Python, PyTorch, TensorFlow, OpenCV, FastAPI, Docker, CUDA, AWS, Modal, DeepStream, Javascript/TypeScript, NodeJS, NextJS, ReactJS, Electron, Tauri, PyQt - Built high-performance real-time object detection systems with NVIDIA DeepStream for edge and GPU deployment. - Developed OCR & document understanding pipelines for scanned documents, engineering drawings, and forms. - Built LLM/VLM-powered AI applications, including multimodal assistants, RAG systems, image analysis, and AI inference APIs. Let's turn your AI idea into a production-ready product.

I am an AI Engineer with 4+ years of experience building and deploying production-ready AI systems across classical machine learning, deep learning, computer vision, NLP, and Generative AI. Unlike many AI developers who focus only on LLMs, I work across the entire AI stack. I believe the best solution isn't always a large language model or an expensive API. Many real-world problems are better solved using classical machine learning or deep learning, resulting in lower infrastructure costs, faster inference, reduced latency, and greater control over your solution. My goal is always to build the most effective system not the most expensive one. Some of the areas I regularly work in include: * Classical Machine Learning (XGBoost, LightGBM, CatBoost, Random Forests, SVMs, feature engineering, predictive modelling, forecasting, anomaly detection, recommendation systems) * Deep Learning (PyTorch, TensorFlow, CNNs, Transformers, Vision Transformers, knowledge distillation, model optimization) * Computer Vision (object detection, image classification, segmentation, OCR, document understanding, face recognition, multi-object tracking, embedding-based search) * NLP & LLMs (RAG, GraphRAG, agentic workflows, fine-tuning, embeddings, semantic search, document QA, information extraction) * Generative AI applications using OpenAI, Anthropic, Gemini, and open-source models * End-to-end AI pipelines from data collection and preprocessing to training, evaluation, deployment, and monitoring I also have extensive experience optimizing AI models for production through knowledge distillation, pruning, quantization, and efficient inference, making models smaller, faster, and more cost-effective for both cloud and edge deployments. On the engineering side, I work comfortably with Python, FastAPI, PostgreSQL, pgvector, asynchronous programming, Docker, GPU acceleration, and cloud deployments. I build complete AI products and APIs that are designed to scale not just research prototypes. Beyond implementation, I enjoy solving difficult research and engineering problems. Whether it's designing a predictive model, improving model accuracy, reducing inference costs, building an intelligent document processing pipeline, or deploying an LLM application, I focus on solutions that are reliable, maintainable, and practical for production. I also lead a team of AI engineers, giving me experience not only in technical execution but also in planning, code quality, mentoring, and delivering projects on time. If you're looking for someone who can understand the problem first, choose the right AI approach, and build a production-ready solution that balances performance, cost, and scalability, I'd be happy to help.

I build production AI systems that businesses run on every day: AI agents, RAG pipelines and LLM workflow automation for companies where a wrong answer costs real money. WHY CLIENTS PICK ME • Expert-Vetted, the badge Upwork awards its top 1% by interview, not by algorithm. 100% Job Success across 70+ projects and six years. • Production, not prototypes. Not proof-of-concepts. Every system ships with schema validation, evals and a regression suite, so it still works six months after launch and you can prove it. • Scale. I have processed 12 million documents through a single RAG pipeline and run agents live in front of real consumers. • I integrate with what you already have. Your CRM, your database, your APIs, your cloud. AWS certified (ML Specialty), equally at home on GCP. • You always know where the project is. A written update whenever something moves and a weekly Loom walkthrough. Clients tell me this is the part they remember. WHAT HAPPENS WHEN THIS GOES WELL A process that eats your team's week runs on its own, and you get the evidence it did not get worse. On an AI underwriting platform used by 15+ insurers across the US, policy review went from days to 3 to 5 minutes at 99.55% precision on the rules that move money. A UK mortgage broker cut manual payslip review by 80%, because the automation only escalates the documents two models disagree on. A UK government-data client got 12 million planning documents extracted, embedded and searchable in under 48 hours at 65% below the GPT-4 baseline cost. Most of my engagements run long-term. The first project automates one workflow; the ones after that tend to automate the rest. WHAT I DO 1. AI agents and workflow automation. Multi-agent systems that take a manual process end to end and make real decisions along the way. Built in LangGraph and Claude with MCP, schema-validated, observable at every step, with a human in the right place by design. I built the assistant inside a UK consumer money app: seven specialised agents, 43 deterministic tools, every financial figure computed by code rather than the model, so it cannot make up a number. 2. RAG and LLM engineering. Retrieval pipelines, knowledge assistants and chatbots over your own data, and cost and accuracy work on LLM systems already in production. 700+ UK planning policy documents summarised weekly, fully automated, at 75% lower LLM cost through context caching and batch APIs. 3. The machine learning and data foundations underneath. Document and data extraction, classification, Python, SQL, pandas and scikit-learn. A private-equity deal-screening agent I built completes the firm's own investment scorecard end to end, backtested against realised fund returns. HOW I BUILD The simplest thing that works, then iterate with evidence. Strong prompting before fine-tuning. An API call before custom infrastructure. A benchmark before an architecture decision. Non-negotiable on every build: • Structured outputs with schema enforcement. If it does not validate, it does not pass • Dual-model verification on high-stakes data • Full observability with LangSmith or Langfuse. Nothing is a black box • A gold-standard eval set built early and regression-tested on every change WHAT CLIENTS SAY "His Loom updates were the highlight of my week! He's great at communicating complex AI concepts to non-technical people and keeps you in the loop every step of the way." Chris Barnes, Co-Founder, Gains App "Adam is an absolute powerhouse of an LLM Engineer. He has first class communication skills which make working with him an absolute pleasure." Sammie Ellard-King, Founder, Gains App "A great balance of personality, professionalism and a deep knowledge of the AI space. He's a strategic thinker who considers the bigger picture." Tom Story, PlannrAI STACK • Agents and orchestration: LangGraph, LangChain, Claude Code, MCP, LangSmith, Langfuse • Models: Claude, OpenAI, ChatGPT, Gemini, Llama, via Bedrock, Vertex and direct APIs • Backend and infrastructure: Python, FastAPI, Postgres with pgvector, Docker, Kubernetes, AWS (ML Specialty certified), GCP • Machine learning: PyTorch, scikit-learn, pandas, SQL • Reliability: Pydantic structured outputs, dual-LLM verification, evals and LLM-as-judge test suites TAKING ON NOW • AI agent and workflow automation builds, end to end • RAG pipelines and knowledge assistants over your own data • Cost and accuracy work on LLM systems already in production • Claude Code and AI engineering enablement for teams adopting AI Expert-Vetted · 100% Job Success · $400K+ earned · 5,750+ hours · 70+ projects

With 5+ years of experience and 150+ successful projects, I help businesses build high-performance Computer Vision systems that work in production — not just in theory. 🚀 What I Build ✔ Object Detection & Multi-Object Tracking (YOLO26, YOLOv12, YOLO11, YOLOv8, DeepSORT, ByteTrack, BOT-SORT) ✔ Real-Time Video Analytics & Surveillance Systems ✔ Face Recognition & Liveness Detection ✔ Image Segmentation (U-Net, DeepLabV3+, Semantic & Instance) ✔ OCR & Document AI (Tesseract, Google Document AI, PaddleOCR) ✔ Industrial Defect Detection & Quality Control ✔ Medical Image Analysis ✔ Traffic & Vehicle Detection Systems ✔ Retail Analytics & Customer Behavior Tracking ✔ Edge AI Deployment (Jetson, TensorRT, CUDA, Docker, AWS) ✔ Model Optimization (FPS, latency, memory efficiency) ⚡ What I Deliver ✔ End-to-end computer vision systems (data pipelines → model serving → deployment → monitoring) ✔ Real-time computer vision systems (detection, classification, tracking, segmentation) ✔ Custom YOLO model training on your own dataset (YOLOv8, YOLO11, YOLO26) ✔ Multi-camera surveillance & smart monitoring systems ✔ Video analytics pipelines with real-time alerting & reporting ✔ Scalable AI infrastructure on AWS (SageMaker, EKS, Lambda, EC2) ✔ Production-grade APIs and backend services ✔ Optimization of existing systems (lower latency, reduced cloud costs, improved reliability) 🧠 Core Expertise Computer Vision · Deep Learning · Machine Learning · Object Detection · Multi-Object Tracking · Image Segmentation · Real-Time AI · Video Analytics · OCR · Data Annotation · Edge AI 🛠 Tech Stack AI & Vision: PyTorch · TensorFlow · Keras · OpenCV · MediaPipe · YOLO variants · Faster R-CNN · Vision Transformers Tracking & Optimization: DeepSORT · ByteTrack · BOT-SORT · TensorRT · CUDA Backend & Deployment: FastAPI · Flask · Docker · AWS · Jetson · REST APIs 🌍 Industries I Serve Retail · Security & Surveillance · Healthcare & Medical · Industrial & Manufacturing · Traffic Management · Smart Cities · Agriculture · Sports Analytics 💡 Why 150+ Clients Chose Me ✔ 100% Job Success Score — Top Rated on Upwork ✔ 5+ years delivering real-world AI systems ✔ Production-ready, scalable solutions ✔ Strong optimization — high FPS, low latency ✔ Clear communication & on-time delivery 📩 Let's Work Together Looking to build a Computer Vision system, Object Detection model, or Real-Time AI solution? 👉 Message me now — I'll help you design the best approach and deliver a scalable, production-ready solution fast. /// The following is just for SEO. Please ignore it /// #computer vision #computer vision engineer #computer vision opencv #machine learning computer vision #deep learning computer vision #computer vision machine learning #machine learning python #nlp machine learning #ocr #computervision #machinelearning #ai # ml #cv #ai #transformers #llm #generative ai #generativeai #opencv #jetson nano #jetsonnano #nvidia #model training #modeltraining #yolo #object detection #objectdetection #model training #modeltraining #training yolo model #trainingyolomodel #objecttracking #object tracking #image annotation #imageannotation #datasetannotation #dataset labelling #datasetlabelling #dataset

With a strong background in machine learning, computer vision, and natural language processing, I have consistently delivered real-world AI solutions across diverse industries. My professional journey spans multiple roles where I have developed and deployed intelligent systems for image and language understanding, focusing on accuracy, performance, and scalability. Currently working as a freelancer, I have built intelligent data extraction pipelines for financial applications by combining advanced computer vision and NLP methods to extract information from bank cheques with high precision. Additionally, I worked on a complex chatbot project addressing the limitations of large language models (LLMs), such as context window constraints. I implemented optimization strategies like history truncation, prompt summarization, and prompt caching, which significantly improved coherence, processing speed, and memory efficiency in generated responses. As a Data Science Fellow at Fellowship.AI, I developed a robust evaluation pipeline to measure confidence levels in LLM outputs using datasets like LiveBench and MMLU. I applied strong evaluation metrics such as Expected Calibration Error (ECE) and BERTScore and utilized stronger LLMs for benchmarking, providing valuable insights into model calibration and reliability. Previously, as a Computer Vision Engineer at Hello Llama, I played a pivotal role in building IoT-enabled safety solutions. I developed end-to-end ML pipelines and Android applications integrating BLE and real-time video streaming, designed to work with radar warnings and dashcam systems. My work focused on real-time object detection of urban infrastructure and safety violations, deploying models on NVIDIA Jetson Nano for low-latency, edge-based inference. I also created a custom helmet detection system with facial focus and chin-strap detection and used sensor mat data to recognize multiple riders on scooters. My responsibilities included complete development cycles—from data collection to annotation, model training, validation on unseen data, and deployment—with comprehensive testing to ensure robustness in production environments. Earlier in my career as a Machine Learning Engineer at Expert Consortium Ltd., I worked on automatic face recognition and liveness detection systems. I designed pipelines capable of distinguishing live individuals from photographs and automatically created datasets for unknown individuals by organizing webcam feeds, labeling them, and training recognition models using LBP. I also built a driver activity recognition system for behavioral monitoring and an object tracking tool that captured snapshots when specific spatial triggers were activated. I utilized GPU acceleration and RabbitMQ to ensure high performance and seamless message passing during training and inference. Throughout my experience, I have demonstrated a unique ability to bridge the gap between computer vision and natural language processing. I have built robust pipelines, optimized LLM performance, engineered edge-deployable vision systems, and created intelligent, real-time applications in safety, finance, and mobility. My strengths lie in developing production-ready ML systems, optimizing performance in resource-constrained environments, and delivering intelligent solutions that scale effectively and meet real-world demands. I have direct Experience working on following topics: C++ Python Scikit-learn Tensorflow Keras OpenCv Embedded device Aws Azure Flutter