● ML engineer · data scientist
I code solutions
I develop ML pipelines
I play with data
Hi, I'm Thamu! I'm a machine learning engineer focusing on practical applied ML/AI solutions and learning how to use technology to solve problems.
The idea behind this blog is to reflect on experiences, tackle technical topics, and improve my written communication. Lately, I've been diving into cloud-native machine learning systems, blending my love for data with the desire to contribute to something of value. I am actively pursuing certifications in my personal time and practicing applying them to real-world problems.
It's a work in progress, just like me! I'm eager to connect with mentors and fellow tech enthusiasts—let's share ideas and grow together. If you have insights to share or would like to discuss interesting problems, I'd love to connect.
// 02 — Writing
How to evaluate RAG and agentic GenAI systems with MLflow 3.x, mlflow.genai.evaluate(), LLM-as-a-judge scorers, custom trace-aware metrics, and prompt A/B tests.
How MLflow 3.x helps productionise GenAI systems with OpenTelemetry-compatible traces, trace-aware evaluation, RAG judges, custom scorers, and prompt registry workflows.
Wie man RAG- und agentische GenAI-Systeme mit MLflow 3.x, mlflow.genai.evaluate(), LLM-as-a-Judge-Scorern, eigenen trace-bewussten Metriken und Prompt-A/B-Tests bewertet.
Wie MLflow 3.x produktive GenAI-Systeme mit OpenTelemetry-kompatiblen Traces, trace-bewusster Evaluation, RAG-Judges, Custom Scorers und Prompt Registry Workflows unterstuetzt.
Comment evaluer les systemes RAG et agentiques avec MLflow 3.x, mlflow.genai.evaluate(), des scorers LLM-as-a-judge, des metriques trace-aware et des tests A/B de prompts.
Comment MLflow 3.x aide a industrialiser les systemes GenAI avec des traces compatibles OpenTelemetry, une evaluation consciente des traces, des juges RAG, des scorers personnalises et la Prompt Registry.
// 03 — What I do
Sitting at the intersection of business and AI, I try to focus on the solution first. The aim is to reach for the right tool for the job, whether that's generative AI, classical machine learning, or a straightforward heuristic. Most of the projects I'm part of run end-to-end, from helping frame the problem with stakeholders, designing and implementing the infrastructure, to monitoring, maintaining, and supporting new features once they're in production.
RAG systems and LLM agents where generative AI genuinely earns its place; classical ML, mathematical optimisation, or statistical methods where they do the real work underneath. In practice that has meant forecasting and demand modelling, operations optimisation, and retrieval-augmented assistants over internal data.
Training pipelines, model serving, drift monitoring, retraining, and governance work. I believe that the principles don't change with the stack, for example solid orchestration, automated testing, and observability that catches problems before users do.
Translate business outcomes into data and AI architectures. I sit between stakeholders and the model, framing the question before a single line of code is written.
Design reliable, cost-aware infrastructure for AI workloads, currently across Azure and Databricks and previously GCP. Terraform-certified, comfortable making infrastructure decisions.
// 04 — Background
// education
Thesis in the area of stochastics studying the stochastic area
Courses: Data mining, Time series, Extreme value theory, Multivariate Analysis, Simulation theory, Biostatistics
Courses: Statistics, Mathematics, Investment management, Economics, Accounting, Finance
// 05 — Certifications
Google Cloud
Google Cloud
Google Cloud
HashiCorp
Microsoft
Microsoft
Microsoft
McKinsey.org
// 06 — Projects
// Transmit
Reach me directly about consulting, collaborations, or a problem you're working on.
