Frontier AI Researcher · Forward Deployed Engineer
I research how frontier AI works and deploy it where it has to work. My practice runs from reasoning models, agents, interpretability and inference to forward-deployed delivery, in any industry.
The recurring question behind my work is how a frontier model capability becomes a system that can establish truth, exercise bounded authority, survive failure and prove an outcome.
I work across the full decision chain. Strategy defines the problem worth solving and the authority the organisation is willing to delegate. Architecture turns that intent into explicit boundaries between identity, world state, context, reasoning, policy, action and evidence. Evaluation establishes whether the system behaves correctly under normal, ambiguous and adversarial conditions. Production ownership makes release, observation, recovery and change part of the design rather than a late operational hand-off.
Agent systems are a central focus because they force these concerns to meet. A useful agent is not simply a model with tools. It is an operating system of goals, state, context, permissions, typed actions, receipts and recovery paths. I design shared platforms and workflow systems so that autonomy is earned through evidence and can be withdrawn without dismantling the service.
I work as a forward deployed engineer: embedded with the people who own the problem, close to real data and real interfaces, shipping a working slice before writing the strategy deck. My hardest production evidence comes from regulated financial services, where decisions are consequential and accountability persists after the model call ends. That experience sharpens the practice; it does not confine it. The same architecture applies wherever AI touches customers, employees, public services, industrial operations, professional judgement or other high-stakes work.
Alongside delivery, I maintain a research programme in machine intelligence, cognition and human–machine convergence. It examines representation, learning, reasoning, agency, consciousness, human oversight and cognitive sovereignty. The research gives the architecture a stronger account of what a system can do, what it cannot establish and where human judgement remains irreducible.
Each area is designed as part of one production system rather than as an isolated advisory discipline.
Problem framing, option decisions, value logic, capability roadmaps and vertical slices that expose the hardest assumption early.
Agent platforms, context compilers, knowledge layers, multi-agent topologies, cloud services and typed action contracts.
Component, route, trajectory, evidence and outcome evaluation, including failure injection and human-review design.
First-principles work on representation, reasoning, agency, consciousness and the design of human–machine institutions.
The work moves through a disciplined sequence, but discovery and evaluation continue throughout rather than becoming one-off phases.
Clarify the user, purpose, system boundary, source of truth, delegated authority and evidence that would change the decision.
Connect real interfaces and realistic data early enough to test latency, economics, permissions and operating failure.
Place deterministic checks, policy decisions, human authorization, model proposals and receipts where each belongs.
Define release evidence, observability, incident response, rollback, change ownership and the conditions for withdrawing autonomy.