Description

Backend and distributed systems engineer with experience building large-scale, low-latency platforms in real-time environments. I design and operate high-throughput backend services and decisioning systems running under strict latency and reliability constraints.

My work focuses on system scalability and performance optimization, including concurrency control, traffic management, and integrating machine-learning models into production systems. I’m particularly interested in improving the efficiency and resilience of complex distributed platforms through thoughtful architecture and performance-oriented engineering.

I collaborate closely with SRE, data, and ML teams to keep backend systems stable under heavy traffic and infrastructure stress.

Technically, I work primarily with C#, Java, Python, and Scala, alongside distributed data pipelines and containerized platforms. I’m motivated by problems at the intersection of scale, latency, and system reliability.

Languages

English
Native or bilingual
French
Fluent
Chinese
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Criteo
Software Engineer — Real-Time & ML Systems
September 2022 - Today (3 years and 9 months)
Paris, France
- Designed, built and operated high-throughput, low-latency backend systems in a globally distributed real-time platform serving 700M+ users, with strict SLA, correctness, and availability constraints.
- Built and maintained latency-critical decision services processing large-scale real-time traffic; redesigned a core decision workflow with improved parallelism and concurrency control, reducing average request latency from 55ms → 25ms.
- ML Inference Infrastructure — Integrated and operated production ML inference pipelines using NVIDIA Triton, serving an LLM-trained classification model processing 500M+ webpages/day.
- AI-powered Analytics Assistant — Developed a production-grade LLM-based analytics agent enabling natural-language querying of large-scale datasets with safe SQL generation and Python-based analysis over Hive and Vertica data warehouses.
- Large-scale Data Pipeline Optimisation — Implemented conditional filtering in an event-driven ingestion system, reducing dataset volume by 40% and saving $1M/year in infrastructure costs.
- Real-Time System Stability — Designed an online decision engine combining real-time signals with historical analysis to dynamically shape traffic during bursts or infrastructure degradation, protecting system stability, SLA, and margin.
C# Python Spark Scala
BNP Paribas
Senior Software Engineer (Backend & Data Systems)
November 2018 - June 2022 (3 years and 7 months)
Paris, France
- Designed and maintained business-critical backend data services supporting analytics and reporting across core finance and strategy domains.
- Owned ingestion and processing components within an enterprise platform, including schema design, data validation, and correctness guarantees.
- Optimised performance and reliability of large-scale processing jobs through query tuning, resource management, and operational monitoring (Spark/Hive, Ambari).
- Collaborated with data, risk, and business teams to translate analytical requirements into robust, maintainable backend workflows.
Plastic Omnium
Data Scientist (Apprenticeship)
September 2017 - September 2018 (1 year)
- Built ML-based anomaly detection models and analytical dashboards to support manufacturing quality processes.
- Delivered analytical insights and contributed to internal workshops on visualization and analytics methods.