SERVICES
AI infrastructure, cloud-native platforms, data engineering, security, and software delivery for organizations operating across regions.
Semiconductor and embedded systems
ENGINEERING SERVICES
Nine services, each with its own page. The description is the scope. The page is where the engagement is spelled out.
01
Design and build the sites and web applications a client’s users operate. The work covers structure, access, and a release path the team can keep after handover.
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02
Design and build the mobile applications a product needs in the field or in a customer’s hand. The work covers the screens, the data those screens may show, and a build the client team can ship.
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03
Protect the systems and the data those systems are allowed to hold. The work covers access control, the paths data takes, and the checks an operator can run after handover.
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04
Turn operational data into reports the business can trust. The work covers the measures, the sources they come from, and a view the operating team can refresh.
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05
Advise on the estate the organization already runs, then stay for the delivery. The work covers cloud and platform choices, the constraints of security and uptime, and a plan the client team can follow.
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06
Prepare data and the models that read it so a decision can be checked. The work covers validation, the features a model is allowed to use, and monitoring after the model is in production.
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07
Put a model into a workflow that already has an owner. The work covers training data, evaluation against a stated measure, and a serving path the operating team can watch.
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08
Put model-backed workflows into the systems people already use. The work covers permissions, evaluation, and approval where an action is sensitive, then handover to the team that runs it.
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09
Add a visual layer where a person needs to see a system while they work. The work covers the device, the data that overlay may show, and a build the client team can support.
View serviceSEMICONDUCTOR AND EMBEDDED SYSTEMS
Semiconductor teams design, verify, manufacture, and test the chips inside AI systems, vehicles, phones, and factories. Embedded teams write the software that turns those chips, sensors, and boards into a product someone can rely on. This section is about that work, and about how MQGen helps companies find engineers who have done it.
Two longer briefings sit on the insights page: how chip work is divided and the software stack inside a product.
Each small rectangle is a die. The gold die is one chip still on the wafer, before it is cut, packaged, and tested.
SEMICONDUCTOR INDUSTRY
A semiconductor is a material engineered so tiny regions can switch, store, and move electrical signals. Those regions become transistors. Billions of them, patterned onto a wafer and cut into chips, are the compute, memory, sensing, and power control inside modern products. The disciplines below are stages of one lifecycle, not separate industries.
Design and testability come before tape-out. Verification starts before silicon exists and continues after it does. Fabrication, probe, and production test turn a design into parts that can ship. Validation closes the loop when the part in the lab disagrees with the model.
Define the chip
Make silicon testable
Prove the design
Build the devices
Screen every die
Debug real parts
System-on-chip (SoC) and application-specific integrated circuit (ASIC) work decides what the chip is for and how its blocks share area, power, and time. Digital design describes that behavior in register-transfer level (RTL) code. Design verification checks the RTL against the specification while a change is still a code change.
Design for test (DFT) adds the structures a finished chip needs so it can be tested at speed and at volume. Scan insertion threads flip-flops into shift registers. Automatic test pattern generation (ATPG) builds patterns for a fault model. Memory built-in self-test (MBIST) exercises on-chip memories. Coverage is the share of faults those tests can actually see.
Functional verification uses simulation, formal checks, and hardware-assisted runs to show the design does what the spec says. Pre-silicon validation happens before wafers exist. Post-silicon validation and debug happen on real parts, when timing, power, and analog behavior leave the model behind. The work is finding the failing condition and the block that owns it.
Wafer fabrication builds transistors through deposition, lithography, etch, and implant at a given process technology. Wafer probe tests each die before it is cut. Production test screens packaged parts. Yield analysis ties a failure back to the process, the design, or the test program so the next lot is better.
Training clusters, inference servers, graphics, and high-performance computing run on accelerators, GPUs, data-center processors, and the memory that can feed them. High-bandwidth memory and advanced packaging sit beside the compute die because moving data has become as hard as calculating it.
These chips control power, motion, sensing, and safety. Power electronics switch high current. Safety-critical electronics are designed so a fault has a defined response. Industrial control parts are built for temperature, electrical noise, and a long service life on a factory floor.
A real process has dozens of steps. The relationship is simpler. Transistors are built in the silicon. Metal layers connect them into the RTL the designers wrote. A bond pad is where the package, and then the board, can reach the die.
The same lifecycle shows up in very different products. An AI accelerator is still architecture, verification, fabrication, and test. So is the power device in a vehicle inverter and the microcontroller in a factory sensor. What changes is the constraint: throughput and memory bandwidth, battery life, functional safety, or years in a harsh environment.
Accelerators and the memory that feeds them
Data-center processors and fast links
Application SoCs, radios, and sensors
Compute, power, and safety electronics
Control, sensing, and power devices
A small processor with a radio and a job
EMBEDDED SYSTEMS
An embedded system is a computer built to do one product’s work. The processor, memory, and interfaces live on a board. Software on that board reads sensors, drives actuators, and talks to other systems. The person using a vehicle, a smart device, a robot, a factory machine, or a connected product sees the function. They do not see the firmware.
Firmware meets the product here: a processor, power, a sensor, a radio, and a connector, tied together by traces on the board.
Bring-up starts at the bottom. The behavior a user depends on lives at the top. Hardware-in-the-loop testing, system validation, and automated tests sit beside the stack and watch it. They are how a team proves the product, not another software layer.
What the user experiences
Braking response, a robot motion, a medical reading, a machine cycle.
Off the board
CAN, Ethernet, USB, Bluetooth, Wi-Fi, and industrial field buses.
Scheduling and services
Deadlines, processes, or a single loop with nothing underneath it.
The board contract
GPIO, SPI, I2C, UART, PCIe, and the pin mux the schematic promised.
First instructions
Reset, clocks, memory, and the debugger session when a pin is wrong.
The physical computer
The processor, sensors, power, and the printed circuit board they live on.
FIRMWARE PATH
Product code on a microcontroller is usually C or C++, written against registers, a memory map, and a timing budget. The language is ordinary. The constraint is the part.
Firmware starts the device and stays close to the hardware. A debugger, a serial log, a logic analyzer, and a scope are how an engineer finds a clock, a pin, or a race.
A real-time operating system schedules work that has a deadline. Embedded Linux fits products that need processes, networking, and a richer stack. Some products stay bare-metal and are better for it.
A microcontroller fits a tight control loop. A microprocessor with external memory fits a heavier software load. An SoC already integrates compute, accelerators, and interfaces. That choice sets the board and the software.
Device drivers and the board-support package are how software reaches the pins. If this layer is wrong, every layer above it misbehaves, even when the application looks finished.
Bring-up is the first time code runs on the real board: power, clocks, reset, memory, and boot. Integration is the long argument between the schematic and the software, settled on the bench.
The bus is part of the product. Automotive teams live on CAN and Ethernet. Industrial teams add field buses. A connected product also needs provisioning, updates, and a link that fails in a known way.
Hardware-in-the-loop testing runs the software against simulated or real signals. System validation and automated tests catch a regression before it ships inside a vehicle, a robot, or a medical device.
Edge computing decides locally, on a power and latency budget a data center does not have. Robotics and industrial automation close control loops. Automotive electronics and medical devices add safety and regulatory weight. Connected products add a radio, an update path, and a failure mode that has to be boring.
HOW MQGEN HELPS
MQGen Solutions is a technology consulting and engineering firm. When a company needs semiconductor or embedded engineers, the useful question is whether a person’s practical experience matches the requirement. Hands-on work matters more than a title or a list of keywords. Theoretical familiarity is not a substitute for a flow, a bench, or a program the person has already been through.
MQGen starts with the job the engineer will actually do. That means the responsibilities, the technologies, the development environment, the tools already in use, the stage of the program, and the deliverables the role is expected to own. A title alone does not describe a DFT block, a silicon-validation bench, or a board that still needs to boot.
The review looks for work a person has done, not a keyword that appears on a resume. Useful evidence is the flow they ran, the failure they debugged, the lab or production setup they worked in, and the testing method they used. MQGen does not put every candidate through an independent technical exam, and a keyword match is not treated as enough.
Two people with the same title can be a poor match for each other. Fit depends on project experience, technical depth, tools, domain knowledge, and how the person works inside the client environment. An engineer who has shipped MBIST on an automotive SoC is a different conversation from one who has only studied scan theory.
MQGen introduces professionals whose experience lines up with the requirement and stays available so the client and the candidate can confirm scope, tools, and working model. The client decides who to hire. MQGen does not guarantee that a search will end in a placement.
These are examples of experience a review can weigh. They are not a checklist every role requires. MQGen supports the conversation through hiring. The hiring decision stays with the client.
CAPABILITY MATRIX
A business need names the outcome. The engineering area names who does the work. The skills are evidence that a person has done that work before. The same job title can sit in more than one row. The row is what the program actually needs.
INDUSTRY NEED
ENGINEERING AREA
DFT and memory test
EXAMPLE SKILLS
INDUSTRY NEED
ENGINEERING AREA
Design and verification
EXAMPLE SKILLS
INDUSTRY NEED
ENGINEERING AREA
Semiconductor and hardware engineering
EXAMPLE SKILLS
INDUSTRY NEED
ENGINEERING AREA
Embedded software
EXAMPLE SKILLS
INDUSTRY NEED
ENGINEERING AREA
Embedded systems engineering
EXAMPLE SKILLS
INDUSTRY NEED
ENGINEERING AREA
Embedded and IoT engineering
EXAMPLE SKILLS
INDUSTRY TRENDS
The figures below describe the industries. They are not MQGen revenue, hiring volume, or a forecast of MQGen’s business. Confirmed results and forecasts are labeled separately. Analyst estimates of embedded systems use a different boundary than semiconductor shipment statistics, so the two sets of numbers are not slices of one market.
Confirmed result
$795.6B
Up 26.2% from 2024. WSTS full-year 2025 results, 6 March 2026.
Confirmed, partial year
$702B
Up 102% from the first half of 2025. WSTS Q2 2026 release, 6 August 2026. Not a full-year total.
Industry forecast
$1.51T
WSTS Spring 2026 forecast, published 2 June 2026. About 90% above 2025. Not a reported result.
Using actual second-quarter shipments and the June forecast’s assumptions for the rest of 2026, WSTS later calculated about $1.66 trillion for the full year. WSTS stated that this calculation is not a new official forecast.
ANALYST ESTIMATE
$585B
IoT Analytics estimate from the Embedded Systems Landscape & Trends Report 2026, published August 2026. The same source forecasts about 5.6% compound annual growth, to roughly $857 billion by 2032. That 2032 figure is a forecast.
CONNECTED DEVICES
21.1B
IoT Analytics estimate, reported with the August 2026 study and summarized on 3 September 2026. The same note forecasts the installed base to pass 50 billion by 2034. The device count is an estimate. The 2034 figure is a forecast.
Data-center buildouts for training and inference are pulling demand toward accelerators, advanced logic, and high-bandwidth memory. WSTS identified logic and memory, including chips for data centers and AI systems, as the main drivers of record 2025 sales.
WSTS reported first-half 2026 memory sales up 305 percent from a year earlier. Its Spring 2026 forecast, published 2 June 2026, projected full-year memory sales above $800 billion. The half-year change is a reported result. The full-year memory figure is a forecast.
Vehicles keep adding chips for driver assistance, electric powertrains, body control, and the cabin. Safety-critical electronics and power devices bring temperature limits, defined fault behavior, and long qualification cycles that a consumer SoC does not.
Factories and robots need controllers that close a loop on time, talk to drives and sensors, and survive the floor. IoT Analytics attributes more than half of embedded-systems demand to automotive and mobility together with industrial and robotics.
Connected products put a processor, a radio, and software that can be updated into equipment that used to be purely mechanical. IoT Analytics estimates 21.1 billion connected IoT devices installed, and forecasts that base to pass 50 billion by 2034.
Edge systems run inference and control next to the machine instead of sending every sample to a data center. The work lands on embedded processors, local memory, and real-time software, with a tighter budget for power and latency.
FOR COMPANIES
Use the MQGen contact form to describe the role, the tools, and the stage of the program. A consultation request on that page is the same working inquiry path.
Discuss Your Technical Talent RequirementsFOR PROFESSIONALS
The careers page lists positions MQGen is actually hiring for. Apply there with a resume. This page does not feature openings that are not on that list.
Explore Career OpportunitiesNext step
Tell us about the system, the constraint, and the outcome you need. We will respond with a practical path forward.
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