Making computers go beep boop—reliably, at scale, and under real-world constraints.
EnBW Energie Baden-Württemberg AG (via Ishango.ai)·Data Platform Engineer → MLOps Engineer
Keeping production ML and data workflows alive across multiple teams at a large European energy utility — deployment, observability, and working out why a pipeline died at 3am.
First author
A vendor-agnostic architecture for agentic incident triage, diagnosis and guarded remediation across data and AI pipelines — assembled from open-source parts rather than bought.
Creator & maintainer
A vendor-agnostic, deterministic-first SDK for evidence-grounded diagnosis and guarded remediation across data and AI pipelines. Pre-alpha and built in public.
Noeud·Founding Machine Learning Engineer (R&D)
The ML and intelligence layer behind FX-risk decision support for African businesses — pricing currency risk that most businesses here carry blind, and forecasting it across multiple horizons.
MinoHealth AI Labs·Machine Learning Engineer
An autonomous tool-using research agent and the distributed platform under it — 20+ scientific tools behind one API, multi-day runs, 20,000+ candidates per batch. It produced four publications.
Evidence, uncertainty, and the boundary between machine inference and human judgement.
Proposes a vendor-agnostic architecture for agentic incident triage, diagnosis, guarded remediation, verification and organisational learning across data and AI pipelines, assembled entirely from open-source components.
A double-blinded validation of the autonomous agent I architected, run against reference data to test whether the system's discovery process holds up when the answers are withheld.
Applies the tool-using agent and its distributed execution layer to broad-spectrum antibiotic discovery — a workload that depends on high-throughput candidate validation and ranking.
—Epigraphs, compiled
# Simplicity is prerequisite for reliability.# — Edsger W. Dijkstra, EWD498, 1975 from systems import Reliability, Simplicity def build(system: System) -> Reliability: if Simplicity not in system: raise Unreliable("you cannot add it later") return Reliability(system)Talk is cheap. Here is what made it into production.
Sitting between data scientists and production: moving completed ML work into operable systems, and keeping them running.
Building the ML and intelligence layer for FX-risk decision support for African businesses.
Architected autonomous, tool-using research systems and the distributed infrastructure they run on.
Side quests in agents, computer vision, and learning systems.
A deep research agent that plans, retrieves, reaches for tools and synthesises — without a human nudging it at every step.
The public, safety-bounded deployment of the research agent — same tool infrastructure, without needing access to any of it.
Detection of crop stress and disease from the air, plus the drone capture and mapping pipelines that fed it. Flown over real farms in the Bono and Savannah regions.
An end-to-end pipeline testing whether gold-market session structure is predictable at all. Mostly an exercise in not fooling yourself with a flattering backtest.
Tokenisation, attention, the training loop — from first principles, so the thing I operate all day isn't a black box to me.
Notes from the lab: methods, results, and useful failures.
Ideas tested out loud, one question at a time.
InnovoTech Lab · Online
Ghana Data Science Summit / IndabaX Ghana
Tech & Beyond Expo · Academic City University
Ghana Data Science Summit / IndabaX 2025 · Ashesi University
I am mostly interested in what happens at the edge of what we can currently explain.
Systems fail in ways nobody designed for, and that failure is usually the most informative thing in the room. I build intelligent systems, watch them meet real data, and treat the gap between what they were supposed to do and what they actually did as the experiment. Most of what I know came out of that gap rather than out of a plan.
Curious about reliability, uncertainty, and how much a machine should be trusted to decide on its own. Open to research collaborations and hard problems.
