Hi, I'm Mohamed Handaoui.
Senior Software Engineer.
I build high-quality mobile and web applications, lead talented teams, and transform complex problems into elegant, efficient solutions.
About Me
I help teams design, build, and scale reliable products. My focus is on clear architecture, pragmatic engineering, and smooth execution—from idea to production—while keeping UX, performance, and maintainability in balance.
- Design scalable, pragmatic architectures and APIs that are easy to evolve.
- Improve performance, reliability, and observability (profiling, caching, tracing, SLOs).
- Establish solid engineering practices: code reviews, testing, CI/CD, and release hygiene.
- Ship end‑to‑end features across web and mobile stacks with a strong balance of UX, performance, and maintainability.
- Mentor engineers, optimize developer experience/CI, and streamline processes to unlock team velocity.
Experience
April 2025 - Present
Tech Lead Software Engineer
PlaytestCloud
Berlin, Germany
April 2025 - Present
Tech Lead Software Engineer
PlaytestCloud
Berlin, Germany
July 2023 - April 2025
Senior Software Engineer
PlaytestCloud
Berlin, Germany
July 2023 - April 2025
Senior Software Engineer
PlaytestCloud
Berlin, Germany
January 2021 - July 2023
Software Engineer
PlaytestCloud
Berlin, Germany
January 2021 - July 2023
Software Engineer
PlaytestCloud
Berlin, Germany
October 2019 - December 2020
Research Software Engineer
IRT b-com
Rennes, France
October 2019 - December 2020
Research Software Engineer
IRT b-com
Rennes, France
March 2019 - September 2019
Research Software Engineer (Intern)
IRT b-com
Rennes, France
March 2019 - September 2019
Research Software Engineer (Intern)
IRT b-com
Rennes, France
July 2017 - September 2017
Software Engineer (Intern)
Swissport
Algiers, Algeria
July 2017 - September 2017
Software Engineer (Intern)
Swissport
Algiers, Algeria
July 2017 - August 2017
Software Engineer (Freelance)
Moussafir
Algiers, Algeria
July 2017 - August 2017
Software Engineer (Freelance)
Moussafir
Algiers, Algeria
May 2016 - September 2016
Software Engineer (Freelance)
Wilab
Algiers, Algeria
May 2016 - September 2016
Software Engineer (Freelance)
Wilab
Algiers, Algeria
December 2015 - May 2018
Software Engineer (Volunteer)
UNICEF
Algiers, Algeria
December 2015 - May 2018
Software Engineer (Volunteer)
UNICEF
Algiers, Algeria
Education
2018 - 2019
Masters in Software for Embedded systems
Université de Bretagne Occidentale, Brest, France
2013 - 2019
Engineer/Masters in Compute and Software engineering
Ecole nationale Superieure d'Informatique, Algiers, Algeria
Technical Skills
Mobile Development
Flutter, Android (Kotlin/Java), iOS (Swift), Cross-platform
Frontend
Angular, React, TypeScript, JavaScript, HTML5, CSS3, Tailwind CSS
Backend
Ruby on Rails, Node.js, Express, Python (Flask, Django), RESTful APIs
Programming
Dart, JavaScript, Python, Java, Ruby, SQL
System Design
Software Architecture, API Design, Database Optimization
Databases & Storage
PostgreSQL, MySQL, MongoDB, Redis, Firebase
DevOps & Tools
Git, Docker, CI/CD, AWS, Firebase, GitHub Actions
Leadership
Agile/Scrum, Team Leadership, Code Reviews, Technical Documentation
Publications
RISCLESS: A reinforcement learning strategy to guarantee SLA on cloud ephemeral and stable resources
IEEE · Mar 9, 2022 ·View Paper
In this paper, we propose RISCLESS, a Reinforcement Learning strategy to exploit unused Cloud resources. Our approach consists in using a small proportion of stable on-demand resources alongside the ephemeral ones in order to guarantee customers SLA and reduce the overall costs. The approach decides when and how much stable resources to allocate in order to fulfill customers' demands. RISCLESS improved the Cloud Providers (CPs)' profits by an average of 15.9% compared to past strategies. It also reduced the SLA violation time by 36.7% while increasing the amount of used ephemeral resources by 19.5%.
Releaser: A reinforcement learning strategy for optimizing utilization of ephemeral cloud resources
2020 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) · Dec 14, 2020 ·View Paper
Cloud data center capacities are over-provisioned to handle demand peaks and hardware failures which leads to low resources' utilization. One way to improve resource utilization and thus reduce the total cost of ownership is to offer unused resources at a lower price. We propose ReLeaSER, a Reinforcement Learning strategy for optimizing the ephemeral resources' utilization in the cloud. ReLeaSER dynamically tunes the safety margin at the host-level for each resource metric. Our solution reduces significantly the SLA violation penalties on average by 2.7× and up to 3.4×. It also improves considerably the CPs' potential savings by 27.6% on average and up to 43.6%.
Salamander: A Holistic Scheduling of MapReduce Jobs on Ephemeral Cloud Resources
CCGrid2020 / IEEE · May 1, 2020 ·View Paper
We present a heterogeneity and volatility-aware holistic scheduler for running Hadoop MapReduce on unused (ephemeral) cloud resources. Our framework consists of three components: (1) A MapReduce task and job scheduler that relies on a global vision of resource utilization predictions, (2) a scheduler-based data placement strategy that improves the data locality, and (3) a reactive QoS controller that ensures customers' service-level agreement (SLA) and minimizes interference between co-located workloads. Our framework reduces the overall execution time by up to 47.6% and an average of 18.7% compared to state-of-the-art strategies.
Schedule time with me
I'm currently available for freelance work and open to discussing new projects. Feel free to reach out!