Backend · AI · Full-Stack
I build production backends and the ML pipelines that run on them. Three-plus years in Java and Spring Boot, shipping two SaaS platforms from domain modelling to production — one an ERP now running across 10+ public university hospitals. Alongside that, two years of applied ML: pipelines from ingestion to inference, peer-to-peer federated learning across a Raspberry Pi cluster, and time-series forecasting on large-scale renewable energy data, published at ACM e-Energy 2025.
Backend Engineering
Java 21 · Spring Boot · REST APIs · Microservices · DDD · Spring Security
Data & Distributed Systems
Kafka · Redis · PostgreSQL · Event-driven architecture · Hexagonal architecture
ML Systems
PyTorch · LSTM/ARIMA · Federated learning · Edge inference · Model deployment
Research Engineer
UM6P · 2024 – now
End-to-end ML pipelines on production IoT systems, and a peer-to-peer federated learning framework on a Raspberry Pi cluster.
Backend Engineer
EnovaR&T · 2023 – 2024
Hospital ERP SaaS across 10+ public university hospitals — DDD microservices, Spring Security, PostgreSQL, Jenkins.
Full Stack Engineer
Z Smart Services · 2021 – 2023
Three client projects shipped end to end — a data-labelling platform and a secure e-learning platform, both in live use.
MSc Big Data & Cloud Computing · Ibn Tofail University · THERMODSET published at ACM e-Energy 2025 — see the full CV →
Core profile
Backend · Data · ML Systems
Backend services, data pipelines, and AI-powered applications in production.
Programming languages
Python · Java 21 · SQL · TypeScript
Strong backend foundations with practical experience across modern web and data systems.
Core stack
Spring Boot · Kafka · PostgreSQL · PyTorch · Docker
Built and deployed across APIs, microservices, distributed workflows, and applied machine learning.
Skills
Backend-first engineering with strong foundations in APIs, distributed systems, data engineering, and applied machine learning.
Backend Systems & APIs
Production-grade backend services with strong API design, authentication, asynchronous workflows, and reliability patterns such as retries, idempotency, and fault-tolerant processing.
Data Engineering & Distributed Workflows
Data ingestion, preprocessing, validation, and event-driven processing across SQL/NoSQL systems, streaming backbones, and observable pipelines.
AI / ML Engineering
End-to-end ML engineering including data preparation, model training, evaluation, deployment, and integration into backend systems. Covers both predictive models and LLM-powered workflows.
Projects
A selection of backend systems, data platforms, and AI/ML projects that demonstrate production engineering, system design, and applied machine learning.
Consultations platform with routing, notifications, and payments. Built for reliable booking flows, clear failure handling, and observable services.
AI-assisted app generation with secure auth and payments. Integrated LLM capabilities with the Gemini API and hardened the platform for production use.
Adaptive learning platform with dynamic scheduling, instructor ranking, and integrations. Focused on dependable day-to-day operations.
Edge ML with peer-to-peer coordination and evaluation loops. Covers data ingestion, training, inference, and monitoring on constrained devices.
Dataset and experimentation pipeline for building analytics. Deterministic preprocessing, quality checks, visualization, and neural baselines to compare models fairly.
Engineering approach
I focus on clarity, reliability, and maintainability: understand the problem, design the system, ship incrementally, and keep production quality visible.
Clarify constraints, interfaces, acceptance tests, and success metrics.
Break scope into weekly thin slices with demos and instrumentation.
Ship the slices with tests, tracing/metrics, and rollback plans.
Document decisions and add the dashboards and alerts that make a system easy for the team to own.
I’m looking for a backend, AI/ML, or full-stack engineering role on a team working on hard problems in distributed systems, data platforms, or applied machine learning. Open to positions across Europe, Canada and the US — I’m happy to relocate, and equally set up to work fully remote. If that sounds like your team, I’d be glad to talk.