Hire the Best Internet of Things Specialists
in the United States

I am a Materials Science Ph.D. and AI/ML consultant specializing in machine learning, industrial AI, materials informatics, chemistry-driven process optimization, and decision-support systems. I help technical teams turn messy experimental, operator, sensor, process, and production data into predictive models, dashboards, and AI workflows that support faster decisions, lower chemical usage, better process control, and measurable operational savings. I have worked directly with CTOs, process engineers, plant operators, project managers, and technical leadership to translate operational problems into deployable AI solutions. My focus is accelerating decision making. What I deliver: • Real-time prediction models for industrial and chemistry-driven processes • Decision-support AI tools for operators, engineers, and managers • Machine learning models for process optimization, quality control, and performance prediction • Transformation of operator logs and process records into clean, model-ready datasets • Exploratory data analysis to identify process drivers, anomalies, and failure patterns • Anomaly detection for sensor, production, experimental, and operator-recorded data • Time-series modeling for process monitoring, forecasting, and live decision support • Tree-based regression and classification models for interpretable industrial prediction • Hierarchical classification workflows for complex scientific and materials datasets • Deployment-ready dashboards and applications for technical users Relevant industrial AI experience: • Built predictive models for real-time process variables, including treatment performance, effluent quality, chemical response, and operational stability. • Developed decision-support workflows that helped operators evaluate process conditions and accelerate treatment decisions. • Converted inconsistent operator logs and process records into structured datasets for machine learning, dashboarding, and deployment. • Created models to support chemical dosing optimization, reducing unnecessary chemical use while maintaining target process outcomes. • Built classification and prediction workflows for complex materials, chemistry, and industrial datasets. • Developed exploratory analytics and anomaly-detection workflows to identify abnormal process behavior and data-quality issues. • Designed model-development pipelines covering data cleaning, feature engineering, model training, evaluation, interpretation, and deployment. • Delivered working applications and dashboards for stakeholders including engineers, managers, operators, and executive leadership. Core expertise: • Industrial AI and machine learning model development • Materials informatics and chemistry-driven process modeling • Exploratory data analysis and process-data diagnostics • Anomaly detection and outlier analysis • Time-series modeling and real-time prediction • Regression, classification, and hierarchical classification • Tree-based modeling, feature importance, and model interpretation • Decision-support dashboard and application development • Scientific data cleaning, visualization, and reporting • End-to-end project execution from raw data to deployed tool Selected project areas: Industrial Process Prediction and Decision-Support AI Developed industrial AI work includes developing machine learning models and operator-facing decision-support tools for real-time process prediction, chemical dosing support, treatment performance monitoring, and process optimization. I converted operator-recorded and real-time process data into structured datasets, predictive models, and user-facing applications that helped operators and engineers make faster treatment, dosing, and process-control decisions. These tools reduced decision time, supported live process monitoring, and identified potential operational savings of $100K–$200K per year through improved chemical use and process control. Materials Informatics and Scientific ML Built predictive models for materials-property estimation, chemistry-performance relationships, and scientific feature engineering. Work includes modeling alloy properties, chemistry-driven performance, and complex experimental datasets. Anomaly Detection and Process Diagnostics Created workflows to identify abnormal sensor behavior, inconsistent operator records, outlier samples, and unusual process conditions. These analyses helped improve data quality and model reliability before deployment. Why Work With Me? I combine deep scientific training with practical AI/ML development. My strength is translating complex technical data into models, dashboards, and decision-support tools that operators, engineers, and managers can use. I can help you move from raw, messy data to a working AI solution that supports better decisions, faster analysis, and measurable operational value.

A good PCB should work outside the lab—not only on the engineer’s desk. I’m a PCB design engineer and embedded hardware specialist with over 10 years of experience developing production-ready electronics for IoT, wearable, medical, industrial, and wireless products. I handle the complete hardware process, including system architecture, component selection, circuit and schematic design, multilayer PCB layout, prototype bring-up, debugging, and manufacturing documentation. I work with STM32, ESP32, Nordic nRF52/nRF91, RP2040/RP2350, AVR, PIC, and Raspberry Pi CM4/CM5. I also have experience with embedded Linux hardware integration, carrier-board design, peripheral interfaces, and system bring-up. My connectivity experience includes Bluetooth, Wi-Fi, LTE, NB-IoT, LoRaWAN, GNSS, NFC, UWB, RFID, Ethernet, USB, CAN, and RS-485. I work primarily with Altium Designer, KiCad, OrCAD, and EasyEDA. I always think beyond simply connecting components according to a datasheet. For wearable and handheld devices, I pay particular attention to power consumption because battery life is part of the product experience—not just a number on the specification sheet. I also consider RF performance, grounding, EMC/EMI, thermal behavior, protection, component availability, mechanical fit, testing, and manufacturability. My goal is simple: design hardware that works reliably, fits the product, and can be manufactured with confidence.

👋 Hi, I’m a CTO of Iomico IoT development company. We have over 10+ years of experience in firmware & and electronics and over 20 years of experience in software development. We successfully completed a number of IoT projects in the aerospace, manufacturing, logistics, retail, automotive, healthcare, and consumer industries. I encourage you to learn more about Iomico in our intro presentation: 👉 cutt.ly/KXdNUcf 💥We specialize in creating advanced hardware, firmware, and software solutions for start-ups and established companies. Moreover, we have a very experienced and trustworthy development team passionate about technology and innovation. That’s why we deliver you the best project result and keep you away from any risks. 🎯 Electronics engineering: ✔️radio-electronic parts research & selection ✔️Schematics ✔️PCB design ✔️Simulations & Analysis ✔️Review of your current designs 🎯 Firmware technologies we use: ✔️C/C++, Verilog, VHDL, Qt, Python ✔️Baremetal ✔️RTOS (Zephyr, FreeRTOS, AWS RTOS, Chibios, RT-Linux). ✔️Embedded Linux (OpenWRT, uCLinux, Yocto, and stand-alone Linux applications/services/drivers) ✔️Protocols: MQTT, TCP, WebSockets, HTTS(S), FTP, UDP, HTTP, SSH, etc. ✔️Wired and Wireless Networking (BLE / Bluetooth, ZigBee/Z-Wave, UWB, Lora / LoraWAN / 6LoWPAN, RFID, NFC, Mesh Networks, Thread, MLM2M, GSM 3G / 4G /5G, LTE, NB-IOT, Wi-Fi, GPS / GNSS (Galileo, GLONASS, BeiDou), PCIe Gen2/3/4, SATA R.3.x, LVDS, SDI, 10/100/1000 Ethernet, USB up to 3.2, HDMI, MIPI-CSI, MIPI-DSI and more). ✔️Design of MCU/CPU firmware (Nordic Semi (NRF52832, NRF52840, NRF5340, NRF9160, NRF7002), Rockchip (RK3399, RK3328), NXP (i.MX8, i.MX9, Layerscape), STM (STM32H7 and others), Microchip/Atmel, Texas Instruments (Sitara AM57x series), Espressif (ESP32, ESP8266), Renesas, Cypress, Infineon, Nvidia Jetson and any others). ✔️FPGA: Lattice, Xilinx, Intel, Microchip. 🎯 Software technologies we use: ✔️Java, Kotlin, PHP, C#, JavaScript (Angular, React, VueJS, NodeJS). ✔️iOS (Swift, Objective C) and Android (Java, Kotlin) programming, AOSP, cross-platform QT, and React Native. ✔️MySQL, PostgreSQL, OracleDB, MSSQL, MangoDB, RedisDB, and other cloud relational and NoSQL databases. ✔️Messaging queue tools like RabbitMQ, Kafka. ✔️Public and private clouds: AWS, Google Cloud, Digital Ocean, OpenStack. ✔️Cloud clustering / horizontal scaling: Docker, Kubernetes. ✔️OTA: Mendor.io 🎯 Edge AI / Computer Vision: ✔️PyTorch, TensorFlow, MediaPipe, OpenCV, Deepstream ✔️Research and development solutions for “smart” edge devices 🎯OTA: Mender.io, RAUC, Esper and etc. Let me explain how everything works: 1️⃣ First of all, we sign an NDA and arrange a conf call to learn more about your project. 2️⃣ If we agree on each other’s terms, you shall make a prepayment of an amount equal to two weeks of my work on the project. This prepayment shall be used as an advance retainer for my services. 3️⃣ Then, I get down to work alone or with my iomico team, and the payments should be done biweekly. 4️⃣ Please pay attention to my hourly rate and that I work only from the Iomico agency. Furthermore, having released the code, I guarantee the support of the developed product. If you’re ready to implement your ideas (no matter how crazy they are), don't hesitate to contact me here or write to my company Partnership Manager, Michael Bychko - m.bychko@iomico.us.

AI can move a project forward fast. It can also create architecture nobody trusts, firmware that almost works, security holes no one noticed, ML models that don’t survive real data, and prototypes that fail the moment they touch real hardware. That is where I create the most value. I help founders, inventors, and technical teams use AI aggressively without letting AI-generated mistakes turn into expensive engineering debt. If you are already using ChatGPT, Claude, Cursor, Codex, Replit, Lovable, or other AI tools, but your project is getting stuck because the output is brittle, insecure, poorly specified, hard to validate, or disconnected from real-world constraints, I can help. Think of me as an AI engineering whisperer: someone who understands both the power of AI and the engineering realities that AI often misses. My strongest fit is not commodity implementation. If your project is fully specified and just needs code written, there are many lower-cost options on Upwork. My value is highest when the system is ambiguous, partially built, AI-generated, technically risky, or crossing the boundary between software, hardware, firmware, data, ML, and operations. I help answer questions like: ▸ Is this AI-generated architecture actually safe to build on? ▸ Will this prototype survive real users, real hardware, real timing, and real edge cases? ▸ Are there hidden security, reliability, integration, or model-validation risks? ▸ What should be rebuilt, what should be kept, and what should be tested first? ▸ How do we turn a promising AI-assisted prototype into a real engineering system? MY WORK TYPICALLY FALLS INTO FOUR AREAS: AI ENGINEERING RESCUE ▸ Review AI-generated code, architecture, workflows, and product plans ▸ Identify brittle assumptions, missing tests, security gaps, and failure modes ▸ Turn prototype chaos into a coherent technical path REAL-WORLD SYSTEM ARCHITECTURE ▸ Design systems that work beyond the demo ▸ Bridge AI, software, hardware, firmware, sensors, wireless links, and operations ▸ Translate unclear product ideas into buildable technical specifications APPLIED ML & DECISION SYSTEMS ▸ Build and validate machine-learning models for noisy, real-world decision environments ▸ Work with time-series data, feature engineering, model evaluation, and out-of-sample testing ▸ Identify leakage, overfitting, regime-change problems, and weak validation methods ▸ Develop ML systems where bad assumptions create real financial or operational consequences EMBEDDED, IOT & HARDWARE-AWARE DEVELOPMENT ▸ Firmware, PCB, sensor, wireless, and real-time system review ▸ STM32, ESP32, Raspberry Pi, BLE, cellular, IoT, industrial, and prototype systems ▸ Debug issues where software assumptions collide with hardware reality I have taken numerous products from concept to production, including embedded systems, IoT devices, industrial controls, data systems, automation tools, and AI-enabled workflows. I also build applied ML models for noisy, high-stakes decision environments, including financial market models that I use in my own trading. That work demands strict leakage control, disciplined feature engineering, out-of-sample validation, model-failure analysis, and constant attention to whether a signal is real or just noise. My background includes MSEE-level engineering training, decades of hands-on product development, and extensive practical use of LLMs, agentic AI workflows, and applied machine learning. Most AI consultants do not understand hardware. Most hardware engineers underuse AI. Most ML prototypes are not tested hard enough against real-world failure modes. I bridge those gaps. Best fit for: ▸ Founders building AI-assisted products ▸ Teams with prototypes that work in demos but not in the real world ▸ Companies using AI-generated code and needing review, hardening, or architecture cleanup ▸ Teams building ML, forecasting, automation, or decision-support systems ▸ Embedded, IoT, robotics, sensor, wireless, or firmware projects where AI alone is not enough ▸ Inventors who need a senior technical reality check before spending serious money The value I bring is practical technical judgment. I help you move faster without fooling yourself. A short review can often prevent weeks or months of wrong-direction development. Relevant technical areas: Embedded systems • AI/ML systems • LLM workflows • Agentic AI • System architecture • Applied machine learning • Time-series modeling • Forecasting • IoT • Firmware • PCB design • STM32 • ESP32 • Raspberry Pi • BLE • Cellular IoT • Real-time systems • Sensors • Wireless systems • Product development • Prototyping • Manufacturing readiness • Security review • Automation • Data pipelines • Python • C/C++