Hire the Best Machine Learning Engineers

I help clients turn AI initiatives into operational systems that people can actually use and trust. I embed with client teams from discovery through rollout—identifying the right opportunity, designing the architecture, writing the code, integrating existing systems, and building the evaluations and human controls required for adoption. Clients bring me in when an important AI initiative needs more than a strategy deck or prototype. Because I handle both architecture and implementation, I can move from executive conversation to production code without the handoffs and delays of a conventional consulting team. CURRENT WORK • Clinical speech AI and multilingual interpretation For a multi-facility medical clinic in Dallas, I am building the data and model pipeline behind a self-hosted multilingual interpretation platform. The current work centers on collecting dialect-rich Arabic speech from clinic workflows and putting it through rigorous human transcription and review. The system supports segmentation, separate ASR and translation review, second-reader adjudication, consent controls, exact model and audio lineage, and reproducible dataset exports. Generic speech and translation APIs often struggle with regional dialects, medication names, dosages, negations, overlapping speakers, and the messy structure of real medical conversations. This pipeline gives the client a controlled way to evaluate and improve specialized models without allowing unverified AI output to become training truth. The same infrastructure can support Spanish, Vietnamese, Farsi, Urdu, and other high-need languages. The deployment roadmap includes model right-sizing, distillation, and quantization to reduce latency and operating cost across clinic locations. • AI property intelligence and geospatial reasoning Determining what can legally be built on a property is normally a fragmented expert-research process. It requires finding the correct municipal regulations, interpreting ambiguous zoning language, identifying the right parcel and district, understanding road frontage and neighboring conditions, and applying those rules to real geometry. For Plan AI, I built and productionized a property-intelligence engine that performs this work across municipal code, parcel geometry, road networks, building footprints, neighboring lots, FEMA flood data, and permit data. The system converts those sources into setbacks, buildable envelopes, risk signals, maps, and the evidence supporting each conclusion. Because an authoritative-sounding AI answer is not enough, I also built the anti-hallucination and geospatial evaluation layers. They reject invented ordinance language, unsupported calculations, unjustified assumptions, incorrect parcel or building matches, invalid envelopes, and contradictions between the model’s explanation and its result. The result is one evidence-backed workflow for understanding what constrains a property and what can potentially be built—while keeping uncertain cases visible for expert review. SELECTED EXPERIENCE • Led the development of an AI underwriting platform for a publicly traded lender with approximately $40M+ in annual revenue. It automated most application decisions while routing the hardest 10–15% to expert underwriters. • Designed and shipped a production payroll platform for Finally, a $100M Series B company, in approximately six weeks. • Helped build and stabilize Refine.ink, an AI peer-review platform used by faculty at leading U.S. universities. • Built SMART on FHIR and HL7 ADT healthcare integrations, voice-driven legal-intake systems, and computer-vision pipelines for identifying industrial weld defects only a few pixels wide. HOW I WORK I begin with the workflow, the people using it, the cost of failure, and the evidence the system must produce—not with a predetermined model or vendor. I can own the complete path from discovery through production, or embed with an existing team to resolve the critical architecture, integration, evaluation, and adoption challenges. As one Upwork client put it: “Got a week’s worth of work done in less than an hour due to Jonathan’s expertise. Would work with him again, no question.”

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.

Hello, I am investing all my time and resources in Upwork ☝ My experience covers data analysis, AI/ML model training, fine-tuning, and deployment to production on AWS, GCP, Azure, or edge devices. ⬣ Skills : GenAI : RAG, Vector databases, LLM finetune, AI Agent/Multi Agent systems. Machine Learning : classification, regression, similarity search. Computer vision : object detection&tracking, pose estimation, image processing. ⬣Programming languages : Python, MATLAB,C#. ⬣ ML/DL LIBRARIES : TensorFlow, Scikit-Learn, Keras, Pandas, Numpy, OpenCV,Pytorch, HuggingFace,Unsloth, Ultralytics. ⬣ Inference engines: llama.cpp, OLlama, LiteRT-LM, TensorRT. ⬣ Certificates : ✅ AWS Certified Solutions Architect Professional ✅DeepLearning.AI Machine Learning Engineer for production I AM READY TO IMPLEMENT YOUR PROJECT AND CONVERT YOUR IDEAS INTO A REALITY!

* Expert Vetted talent in Upwork with 100% job success rate. * I am looking for long term work in solving problems with Machine Learning solution. * I have been working on Machine Learning for over 5 years. * My area of expertise in Machine Learning area are: Computer Vision and NLP. * My live projects include: 'Detect Products of Super-market shelves', 'Detect sharp objects from x-ray image', various Image classification models like to classify inside/outside House, Shoes(of different materiel), 'Text classification' of various articles. * I have also worked on stock forecasting LSTM model using stock data and sentiment data. * I have a certification from Udacity in "Self Driving Car Engineer" Nano Degree

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.

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