Most software engineers working on AI have never sized a radiator or calculated oil quantity for a 33 kV transformer. Most engineers who have, never made it to CUDA. The overlap is where I am useful.
I started as a design engineer for power equipment — power, earthing and distribution transformers and reactors up to 5 MVA at 33 kV, designed to customer specification and to IS and BS standards. Winding and core-cutting instructions, oil quantity and conservator selection, cooling and radiator calculations, and the AutoCAD drawings the shop floor actually built from. That work taught me something a simulation never teaches on its own: an answer is only correct if the physical thing it describes can be manufactured and will not overheat.
From there I moved into industrial instrumentation and machine vision — LabVIEW control systems synchronising conveyors, solenoids, PLCs and pressure switches on live production lines — then into medical imaging, building a GPU-accelerated OCT retinal analysis platform, then automotive ADAS, where I led AI-driven surround-view and voice-interface features on Renesas R-Car silicon.
Between July 2024 and August 2025 I worked on CUDA-EVAL with Turing, designing and reviewing GPU-programming evaluation tasks used to train and benchmark frontier language models. It is the same discipline as engineering design review applied to a new object: state the problem unambiguously, define inputs, constraints and acceptance criteria up front, produce a validated reference solution, and judge other engineers' submissions on technical correctness rather than plausibility.
The thread through all of it is modeling, simulation and validation: build a model of the thing, check it analytically, then check it against the real system — and be honest about the gap. I document as I go, because a result nobody else can reproduce is not a result.
Areas I have shipped production work in — not a list of things I have read about.
Transformer and reactor design to customer specification and IS/BS standards — magnetics, winding and core design, thermal and cooling calculation, insulation arrangement, and the production drawing set.
Building the model, defining operating conditions, and validating results analytically before trusting them — across MATLAB, LabVIEW instrumentation, robotic simulation and flight-simulation environments.
Schematic capture through layout and bring-up for 8/16/32-bit designs — high-current drive, sensor front ends, industrial buses, and the firmware that exercises them.
Closed-loop machine control on live production lines — motors, pneumatics, PLCs, pressure and proximity sensing — with multiple subsystems synchronised into one coherent data stream.
Profiling and rewriting the hot path — kernel design, memory coalescing, stream overlap, zero-copy CUDA/OpenGL interop — and getting neural networks and language models to run inside real latency and thermal budgets.
Problem definition with explicit assumptions and acceptance criteria, peer review of other engineers' work for correctness and completeness, and methodology documentation that lets someone else reproduce the result.