Curriculum vitae
Gabin Maxime Nguegnang
AI Research Scientist & AI/ML Engineer
Munich, Germany · gmnguegnang@gmail.com
Experience
AI Research Consultant: LLM reasoning and agentic AI evaluation
Outlier AI and Mercor · Remote, Germany
- Find where a language model's step-by-step reasoning breaks on hard STEM problems and write the correction, which becomes the training signal (RLHF) for the next, more accurate version.
- Grade AI agents on tool choice and on staying on track across long tasks (ReAct-style reasoning, function calling, long-context management), and settle the ambiguous cases against a written rubric.
- Design the scoring rubrics and lead peer review, so prompt engineering stays consistent across a distributed group and hard mathematical content turns into training data the team can trust.
Doctoral research and teaching assistant
Ludwig Maximilian University of Munich · Munich, Germany
- Established the conditions under which stochastic gradient descent is guaranteed to converge, showing engineers how aggressively they can train before a run falls apart.
- Delivered an explainable-AI (XAI) project that flags failures early in industrial power plants and gives the reason behind each alert. Reached F1 0.99 with 6 ensemble learners and SHAP, and held fairness across sites (Disparate Impact Ratio 0.95).
- Taught postgraduate courses in optimization, machine learning, deep learning, and data science, and defended a disputed proof step through to acceptance by the Springer Nature editorial board.
Doctoral research and teaching assistant
RWTH Aachen University · Aachen, Germany
- Derived practical learning-rate conditions for gradient descent on deep networks that do not shrink exponentially as the network gets deeper, removing a limit that kept the earlier theory out of practical use.
- Backed the theory with large-scale PyTorch training runs on high-performance computing infrastructure, spreading the workload across nodes to cut the runtime of each experiment.
- Taught continuous optimization, mathematics of data science, and higher mathematics, and built course materials that made advanced theory concrete for engineering students, within a research group of 9 nationalities.
Machine learning intern
Group One Holding Company · Limbe, Cameroon
- Analyzed telecom fuel-consumption data to pinpoint the root cause of fuel loss and compared 4 machine learning models (Gradient Boosting led at 98% Nash efficiency).
- Deployed the winning model as a Flask web application with a monitoring dashboard, securing 84,617 liters of fuel.
- Automated log ingestion, cutting reporting time from days to seconds, and tracked API success rates and processing latency on the dashboard.
- Mapped operational constraints with base-station technicians, then presented the findings, the model results, and the case for rollout to company managers and the operations director.
Education
PhD, Applied Mathematics: deep neural networks optimization
LMU Munich and RWTH Aachen University, Germany
Convergence analysis for training deep neural networks. Published in Springer Nature (2024) and SampTA 2023.
Co-operative MSc, Industrial Mathematics: machine learning
African Institute for Mathematical Sciences (AIMS), Limbe, Cameroon
Mastercard Foundation Scholars Program award, 2017.
MSc, Applied Mathematics: dynamical systems and modeling
University of Yaoundé I, Cameroon
Optimization and numerical analysis.
BSc Honours, Applied Mathematics
University of Douala, Cameroon
Publications
- Convergence of gradient descent for learning linear neural networks Springer Nature: Advances in Continuous and Discrete Models, 2024
- Analysis of stochastic gradient descent for learning linear neural networks SampTA 2023
- Anomaly detection in power generation plants using machine learning and neural networks Applied Artificial Intelligence, 2020
- Predicting fuel consumption in power generation plants using ML and neural networks ICECET 2021
Skills
Programming and data
Python (pandas, NumPy, SciPy, scikit-learn) · PyTorch · SQL · PostgreSQL · pgvector · FAISS · FastAPI · Uvicorn · Pydantic · Flask · REST APIs · Bash
Agentic AI, LLMs, and RAG
AI agents · Multi-agent orchestration · LangGraph · LangChain · MCP · DSPy · RAG · CRAG · ReAct · Chain-of-Thought · Long-context management · Hugging Face (Transformers, TRL, PEFT) · LoRA · QLoRA · SFT · RLAIF · DPO · RLHF
Evaluation and tracking
Evaluation framework design · Live evals · Failure-mode analysis · Root-cause analysis · Red-teaming · Rubric-based evaluation · Preference labeling · Quality gates · Recall@k · ALCE · RAGAS · Prometheus 2 · MLflow
Machine learning and research
Deep learning · Statistics · Non-convex optimization · Convergence analysis · Bayesian optimization · Experimental design · Gradient Boosting · Random Forest · SVM · Anomaly detection · Explainable AI (SHAP) · NLP · Operations research solvers
MLOps, DevOps, and cloud
Docker · GitHub Actions · GHCR · Kubernetes · Azure AKS · AWS · Google Cloud Vertex AI · OpenTelemetry · LangSmith · Git · pytest · Distributed training · HPC · Agile (Scrum)
Certifications
- Advanced agent coding Outlier AI · Jan 2026
- Advanced prompt engineering Outlier AI · Dec 2025
- Generative AI with large language models DeepLearning.AI / Coursera · Oct 2024
- Machine learning engineering for production (MLOps) specialization DeepLearning.AI · May 2024
Memberships and awards
- Munich Center for Machine Learning (MCML), member Jun 2024 – present
- Community of Computational and Mathematical Methods in Data Science, member Dec 2019 – present
- Mastercard Foundation Scholars Program award Aug 2017
Languages
- French Mother tongue (C2)
- English Proficient (C1)
- German Professional working proficiency (B2+)