Hire the Best Model Tuning Specialists

Greetings, I am a Senior AI Engineer and Data Architect specializing in Machine Learning, Deep Learning, Federated Learning, AI Agent Development, LLMs, AI Automation, Automated Workflows (n8n), and Data Architecture. I focus on building scalable, secure, and production-ready AI and data solutions that solve real business problems. My core strength lies in designing end-to-end AI and data systems, from data ingestion, data modeling, and data processing to model development, deployment, automation, and business intelligence. I have strong experience in LLM-based RAG systems, computer vision, privacy-preserving AI using Federated Learning, and modern data platforms. I also work on enhancing AI security through Blockchain integration where applicable. - Key Expertise: * Data Architecture & Engineering * Data Lake, Data Warehouse, and Lakehouse Architecture * Data Modeling: Star Schema, Snowflake Schema * ETL/ELT Pipelines and Data Integration * Data Governance, Data Quality, and Metadata Management * Batch and Real-Time Data Processing * Big Data Platforms: Spark, Hadoop, Cloudera CDP * DataOps, CI/CD, and MLOps - Generative AI and Automation * LLMs, RAG systems, AI chatbots * AI Agent Development * Agentic AI (LangGraph, CrewAI, MCP) * Automated Workflow Development * n8n Workflow Automation * AI workflow automation and integrations * API integration and orchestration - Advanced AI * Federated Learning * Secure AI systems with Blockchain integration - Machine Learning * Regression models, Decision Trees, SVM * Ensemble methods: Random Forest, Gradient Boosting, XGBoost * Probabilistic and distance-based models: Naive Bayes, KNN - Deep Learning * ANN, CNN, RNN, LSTM, GAN * Model optimization and deployment - Computer Vision * Image classification, object detection, segmentation I am passionate about collaborating with clients to deliver robust, efficient, and future-ready AI and data solutions. If you are looking for a Senior AI Engineer and Data Architect who combines research-level expertise with real-world implementation, I would be happy to discuss your project.

If your model isn’t performing well, the problem is often the data — I help fix that. I specialize in data-centric computer vision systems: improving detection accuracy, reducing false positives, refining datasets, and deploying stable real-time AI pipelines on edge and mobile devices. I build end-to-end computer vision workflows from dataset preparation and model training to real-time Android deployment. What I help with: ✓ Reducing false positives and missed detections ✓ Dataset QA, cleaning, validation, and deduplication ✓ Improving label consistency across large-scale datasets ✓ Building feedback loops between model predictions and dataset correction ✓ Embedding-based similarity and clustering for duplicate detection ✓ Segmentation mask processing and structured object extraction ✓ Real-time object detection and tracking systems ✓ Improving tracking stability and frame-to-frame consistency ✓ Edge/mobile AI inference optimization ✓ Real-time Android deployment workflows Real-world experience: ✓ Built and deployed computer vision systems for fitness applications ✓ End-to-end pipeline development: dataset preparation → training → inference → Android deployment ✓ Real-time on-device inference pipelines ✓ Barbell tracking and repetition counting ✓ Skeleton-based motion analysis ✓ Equipment classification and tracking consistency ✓ Turning raw detections into stable, usable systems Technical stack: ✓ YOLO (training, fine-tuning, evaluation) ✓ OpenCV, PyTorch, Ultralytics YOLO, SAM ✓ TensorFlow Lite (TFLite) and ONNX deployment workflows ✓ Android Studio, CameraX ✓ CVAT, Label Studio, Roboflow, Labelbox, Supervisely ✓ QGIS, GeoTIFF, GeoJSON, MultiPolygon

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 build specialized ML models for time series data, paired with conversational AI interfaces that let users query their data, generate forecasts and detect anomalies in natural language — replacing static dashboards with direct, interactive access to the data and models. PhD in statistics, 15+ years in applied ML, with peer-reviewed publications. AWS-certified ML specialist and generative AI developer. Deliverables: - ML models for time series forecasting, anomaly detection, clustering and classification - AI agents with Amazon Bedrock AgentCore and Strands Agents - Serverless ML solutions on AWS Lambda with Amazon API Gateway endpoints - Custom Amazon SageMaker containers for training and inference - Integration with data backends such as ClickHouse, PostgreSQL and S3 Tools & frameworks: Python · SQL · Docker · PyTorch · TensorFlow · scikit-learn · XGBoost · LightGBM · CatBoost · Amazon Bedrock (AgentCore, Strands Agents) · Amazon SageMaker (AutoPilot, BYOC) · AWS Lambda · Amazon API Gateway · ClickHouse · PostgreSQL · RDS · S3

ML Engineer — Computer Vision, Claude/LLM Agents & Audio AI I build deep learning systems across vision, language, and audio — and ship them to production, not just notebooks. I've trained models from scratch, fine-tuned transformer architectures, and deployed to Core ML on real client engagements (including a 400+ hour model-build contract). 5.0 stars across every reviewed project. Computer Vision Image classification, detection, and feature extraction with CNN and transformer (ViT/AST-family) architectures. My core audio work runs on spectrogram based vision models, so the same deep-learning toolkit — convolutions, attention, transfer learning — carries directly into image and video tasks. Claude / LLM Agents & Automation AI agents, automation workflows, and reasoning assistants built on Claude and OpenAI APIs. RAG pipelines, tool-use/agentic systems, and LLM integrations that connect models to real data and real actions. Audio & Speech AI Instrument/audio classification, feature extraction, and transformer fine-tuning (AST, Wav2Vec2, HuBERT), plus generative audio with AudioCraft/MusicGen. Built multiple audio models end to end — clients describe me as central to their ML work. Deployment & Engineering TensorFlow/PyTorch → Core ML export, FastAPI inference services, and optimized real-time / batch pipelines. I handle the messy export and optimization steps end to end. Top Rated · 100% Job Success · 5.0 across all reviewed jobs. I move fast and build things that actually deploy.

Need a Computer Vision Engineer who can turn image or video data into a reliable production system? I build real-time computer vision and machine learning pipelines for object detection, tracking, segmentation, pose estimation, OCR/ANPR, industrial inspection, and video analytics. My core stack is Python, PyTorch, YOLO, OpenCV, ONNX Runtime, TensorRT, CUDA, FastAPI, and Docker. 3+ years in Computer Vision and ML 100% Upwork Job Success Score with consistent 5-star feedback $20K+ delivered across Upwork and direct engagements Selected results: * Built and evaluated an automotive component-inspection system using 20,000+ images * Developed real-time posture and movement analysis with custom keypoint logic, calibration, temporal smoothing, and CPU-friendly inference * Built multi-camera vehicle detection, persistent tracking, ANPR/OCR, and event logging * Optimized a background-removal pipeline from 2+ seconds to 0.8 seconds and reduced 4K image-saving time from about 13 seconds to 2 seconds I can help with: Computer Vision and Deep Learning * Object detection, classification, instance/semantic segmentation, pose estimation, and tracking * YOLO training, transfer learning, dataset preparation, augmentation, evaluation, and error analysis * RTSP, CCTV, webcam, multi-camera, and recorded-video analytics * OCR, ANPR/ALPR, document vision, barcode, label, and product recognition * Industrial inspection, defect detection, image registration, measurement, and anomaly detection * Research-paper implementation and custom algorithm development Inference Optimization and Deployment * PyTorch to ONNX conversion and ONNX Runtime/TensorRT acceleration * FP16/INT8 quantization, batching, profiling, memory reduction, and CPU/GPU optimization * FastAPI/REST APIs, Docker, Redis workers, databases, dashboards, and Electron integration * Using NVIDIA Triton Inference Server or NVIDIA Deepstream for pipelines and delivery * Deployment on Windows, Linux, AWS, RunPod, local servers, and edge hardware You receive tested, maintainable code, clear documentation, and a system designed around your target accuracy, FPS, latency, privacy, hardware, and operating environment. Send me your sample images/video, current code or model, target hardware, and required accuracy/FPS. I’ll recommend the most practical route from prototype to deployment.