Ayoub Ben

🔧 Critical Alert Management System – SFR

Context:

Participation in the development of a centralized incident monitoring platform for critical servers and applications, aiming to consolidate multi-source alerts (Prometheus, Shenken, Vtom...) and automate their processing to improve responsiveness and reduce manual interventions.

🧩 Key Achievements:

🔹 Alert Processing & Automation

Development of a Python engine consuming real-time Kafka streams.

Design of asynchronous microservices (FastAPI, asyncio) for automatic ticket creation and corrective action execution.

Implementation of concurrency management with semaphores.

🔹 Monitoring & Visualization

Design of Grafana dashboards and a frontend interface (SvelteKit + Cursor AI) for real-time visualization.

Monitoring via Prometheus & centralized logs via the ELK Stack.

Automated log pipelines with Ansible + GitLab CI.

🔹 Remediation & Orchestration

Automatic triggering of corrective actions via Airflow (API tickets, escalation).

GitOps deployment with FluxCD, Kubernetes + Helm orchestration.

Endpoint security via Google Cloud Armor.

🔹 Cloud Infrastructure

Provisioning of infrastructure on GCP via Terraform (GKE, IAM, storage...).

CI/CD automation with GitLab & GitOps integration (FluxCD).

🧪 Technical Environment:

Languages & Frameworks: Python, FastAPI, asyncio, SvelteKit

Databases: MongoDB

Cloud & IaC: GCP, Terraform, Google Cloud Armor

Orchestration: Kubernetes, Helm, Docker

CI/CD & GitOps: GitLab CI, FluxCD

Monitoring: Grafana, Prometheus, Kibana, ELK

Data & Automation: Kafka, Airflow, Ansible

Methodology: Agile Scrum

Implementation of a centralized solution to monitor and reduce IT infrastructure vulnerabilities by consolidating server/workstation data. Design of an interactive dashboard with natural language search for technical and business teams.

🔹 Development of a Python engine for data extraction (SQL Server, REST API), SQL optimization & Elasticsearch indexing.

🔹 Automation of Elasticsearch updates with asynchronous processing (asyncio, aiohttp) to reduce latency.

🔹 Creation of secure APIs with FastAPI exposing consolidated data. OpenAI integration for RAG, enabling intelligent log searches.

🔹 AI interface with Streamlit and advanced visualizations via Angular & Kibana, including predictive analysis.

🔹 Automated archiving of histories to AWS S3 with multithreading to speed up transfers.

🔹 Observability ensured via Prometheus, Grafana, ELK, and Nagios.

🔹 Quality standards (Black, Pylint, Bandit), regular code reviews, and complete documentation of pipelines.

Stack: Python, FastAPI, Elasticsearch, SQLAlchemy, Streamlit, Pandas, OpenAI API, AWS S3, Kibana, Prometheus, Nagios, Ansible, Git, Bitbucket, SQL Server, Jira, Agile Scrum.

🛠️ Python / DevOps Engineer – Automation & CI/CD on Azure

Responsible for implementing collaboration tools, automating build/deployment processes, and delivering new versions of the Radar application in a Microsoft Azure cloud environment.

🔹 Cloud Infrastructure & Containerization:

Setup of a complete environment on Azure (AKS, ACR, Redis…).

Development of Dockerfiles and Kubernetes manifests (services, ingress, secrets).

Complete management of AKS for different environments (dev, pre-prod, prod).

🔹 CI/CD & Deployment:

Implementation of Jenkins CI/CD pipelines, creation of custom jobs based on client needs.

Automated delivery to PTC Windchill for application version management.

🔹 Automation & Scripting:

Generation of build files with Bazel (Starlark) replacing Make.

Creation of Python scripts to generate Radar-specific configurations.

Automation of C++ code updates with Python, Shell scripting for various server tasks.

🔹 Collaboration & System Configuration:

Installation & configuration of Gerrit (code review), LDAP, SMTP, iptables.

Setup of periodic garbage collection tasks for server optimization.

Writing technical documentation on tools (Gerrit, Bazel) and developer support.

Technical Stack: Azure, Kubernetes (AKS), Docker, Python, Bazel, Starlark, Shell, Jenkins, Gerrit, PTC Windchill

Environments: CentOS 7, Windows

Tools: Codebeamer, VectorCast, VSCode

Methodology: V-Model