Védric Loïc EBOA EBOLO

Steering the integration and management of the Instana solution from IBM to optimise the observability of critical systems. Coordinating deployments, planning upgrades, developing CI/CD pipelines, and advanced management of complex environments while ensuring the draughting of detailed technical documents. ● Advanced configuration of agents on different operating systems (Linux, Windows) and integration with complex environments (VMs, Kubernetes, Docker). ● Draughting of detailed architecture documents describing data flows, network interconnections, monitoring components, and integration procedures. ● Development of playbooks for configuring Instana (start, stop, backup, install...), managing secrets with Vault, and automated environment updates. ● Management of the attachment, creation and expansion of persistent volumes (PVCs) on K3s clusters in the private cloud, with verification of CSI compatibility. ● Development of CI/CD pipelines to automate the deployment of Instana and associated agents, with validation testing and integrated reporting. ● Implementation of failover strategies between availability zones (AZ) based on business needs, with validation of disaster recovery tests (DRP). ● Coordination of deployments of new Instana functionalities with the technical teams to minimise the impact on production. ● Implementation of Terraform configurations to automate the creation and management of Kubernetes clusters (K3s) and persistent volumes (PVCs) in the private cloud, while respecting CSI compatibility constraints and ensuring the resilience of critical systems. ● Creation of Ansible playbooks to automate the deployment of Instana agents, the management of Vault configurations and the updating of observability environments, thus reducing manual errors and accelerating deployment cycles. ● Planning and execution of Instana upgrades, with validation of compatibility and management of impacts on existing environments.

Management and optimisation of AWS infrastructure and monitoring of MB2 and MyCard products with Dynatrace, implementation of SRE practices, and automation of processes to improve resilience, security, and system performance. ● Dynatrace Monitoring Optimization: Fine-tunes Dynatrace for AWS monitoring, particularly ECS with OneAgent, improving observability, and reducing alert noise. Integrates Dynatrace with Opsgenie to automate alert escalations for efficient incident management. ● PRA Planning: Collaborates with SecOps to design automated PRA (Plan de Reprise d'Activité) strategies, ensuring high availability and rapid service restoration. ● Alert Management and Escalation: Develops multi-tiered alert and escalation workflows in Opsgenie, creating detailed response strategies and visual schematics aligned with incident management protocols. ● Monitoring Architecture Design: Architect scalable monitoring solutions with Dynatrace, optimizing AWS environments, including ECS and Fargate, for proactive performance management and anomaly detection. ● PostgreSQL Monitoring: Configures Dynatrace for PostgreSQL, setting up custom dashboards and alerts for performance metrics, ensuring high database availability and reliability. ● Terraform Code Review: Reviews and refactors Terraform IaC for best practices, optimizing resource configurations, and integrating automated checks to improve deployment reliability and security. ● CI/CD Pipelines with GitHub Actions: Designs GitHub Actions CI/CD workflows, automating infrastructure and application deployments. Integrates testing, linting, and security scanning to ensure best practices. ● KPI Reporting: Develops KPI generation for Opsgenie, analyzing incident management metrics to support continuous service improvement. ● Postmortem Leadership: Lead postmortem analysis, documenting incidents for root cause identification and prevention strategies, promoting transparency and improvement across teams.

Helping to improve application resilience (autoscaling, circuit breaker) in line with application requirements (SNCF Connect). ● Developing traffic simulation scenarios in Scala using Gatling: Creating realistic surge simulations to test application performance and resilience. ● Improved application resilience by integrating autoscaling on Kubernetes using Keda, which ensured optimal performance during periods of high traffic. ● Created Bash scripts with kubectl for manual management of circuit breakers, significantly reducing recovery time and preserving the user experience. ● Creation of custom dashboards on Datadog for real-time monitoring, improving responsiveness and operational decision-making. ● Efficient table management in AWS DynamoDB to store and manage infrastructure states using Terraform. This helped maintain consistency and centralised management of resources, making it easier to deploy and maintain cloud infrastructure. ● Designed and deployed circuit breaker strategies across various services using Istio, defining fault tolerance rules for each service to prevent cascading failures and ensure the resilience of the overall system. ● Alerting Configuration: Define alerting thresholds and conditions based on key performance metrics, such as CPU usage, memory consumption, and latency, and configure alerting channels to notify relevant stakeholders promptly. ● Enhanced AWS infrastructure by implementing Terraform backend with state stored in DynamoDB, ensuring secure and consistent management of infrastructure configuration and state across teams and environments. ● Orchestrated CI/CD workflows, including version control, automated testing, and deployment strategies, to improve software quality and delivery. ● Managed and administered MongoDB clusters, performance tuning, and backup/restore procedures.

Thales is a French electronics group specialising in aerospace, defence, security and ground transportation, with headquarters in the La Défense district of Paris. ● Configured the cluster's Nginx Ingress Controller to facilitate accurate HTTP/HTTPS routing, resulting in a notable 43% improvement in routing efficiency to targeted services. ● Developed Terraform scripts to establish AWS Auto Scaling groups for Kubernetes worker nodes. ● Reduced AWS operating expenses by 20% by using AWS Cost Explorer and Kubecost to optimise scalability and resource utilisation. ● Development and implementation of a Virtual Private Cloud (VPC) for resource isolation, as well as the creation and implementation of public and private subnets to segment traffic and guarantee security on AWS. ● Implemented EC2 instance deployment and management by provisioning and configuring instances for Kubernetes worker nodes, while using Auto Scaling to automatically adjust the number of instances based on fluctuating workload. ● Reduced the size and boot time of Docker images, saving 30% in resources and increasing container launch speed by 20%. ● Monitoring Setup: Configure Datadog agents on Kubernetes nodes and services to collect metrics, traces, and logs for comprehensive monitoring. ● Integrate Vault for Secret Management: Implemented Vault to securely manage sensitive data. ● Implement Dynamic Secrets with Vault: Configured Vault to dynamically generate database credentials. ● Secure Service-to-Service Communication with Vault: Utilized Vault's transit secret engine for encryption in transit between microservices. ● Implement Elastic Beanstalk for Zero-Downtime Deployment: Set up AWS Elastic Beanstalk to manage and automate the deployment of applications, ensuring zero downtime during updates and scaling operations.

Implementation and management of CI/CD pipelines and secure infrastructures on GCP to guarantee scalability, availability and security of services Automating massive data processing with BigQuery, Pub/Sub and Cloud Functions, with the aim of optimising performance and meeting business requirements. ● Creation of buckets in Storage for data storage, with configuration of lifecycle policies. ● Creation of service accounts with appropriate authorisations in IAM to secure access, with identification key management for application authentication. ● Implementation of asynchronous tasks in Cloud Tasks for background processing, with configuration of task scheduling and prioritisation. ● Provided user support on tools such as JFrog, Gitbook, Github, Terraform and CloudCraft to ensure effective use and resolve any issues encountered. ● Integrating secrets management with Vault via Terraform to ensure the security of sensitive data in GCP environments. ● Use of GitHub Actions to automate CI/CD workflows, including build, test and deployment. ● Automated backups and disaster recovery for PostgreSQL databases, ensuring data availability and resilience. ● Implement Vault Agent Auto-Auth for GCP Workloads: Deploy Vault Agent alongside GCP workloads to automate authentication and authorization processes, ensuring seamless access to Vault secrets with Terraform-driven configurations. ● Implement Vault Transit Secrets Engine for Data Encryption: Utilize Vault's Transit Secrets Engine to encrypt and decrypt data at rest within GCP, enhancing security and compliance with Terraform-managed configurations. ● Implementation of Google Cloud Functions to process events in real time, with configuration of triggers to automate data processing workflows. ● Integration of Cloud Functions with Cloud Storage to trigger processing each time files are added to buckets, enabling automatic data processing.

Experience focused on improving predictive maintenance in the French Army using maintenance data analysis (MCO-T) and advanced modelling techniques. ● Implementation of specific studies based on MCO-T data to analyse a large fleet and size maintenance, using Python with the NumPy, Pandas, and Scikit-learn libraries, as well as Jupyter Notebook for documentation and visualisation. ● Improved modelling of the link between vehicle use and maintenance load using advanced regression and classification techniques with Python and Scikit-learn, exploiting Matplotlib and Seaborn to visualise the results. ● Testing prediction models based on HUMS on-board sensor data using TensorFlow or PyTorch to build deep learning models, with Jupyter Notebook for experimentation and analysis of results. ● Creating data processing pipelines to encode categorical variables into numerical variables using Scikit-learn, and integrating these pipelines into automated workflows via Jupyter Notebook or Python scripts. ● Optimising model performance by adjusting the hyperparameters of machine learning algorithms using techniques such as Grid Search or Random Search to improve prediction accuracy. ● Development of interactive dashboards for the visualisation of predictive maintenance key performance indicators (KPIs) using Dash or Streamlit, enabling users to monitor the health of the fleet in real time. ● Implementation of automated tests to validate the robustness and accuracy of predictive models using Python testing frameworks such as PyTest, ensuring ongoing reliability of predictive maintenance solutions. ● Data extraction and manipulation from relational databases using SQL to prepare maintenance data (MCO-T) and HUMS on-board sensors, including the creation of complex queries to aggregate and filter relevant data for analysis. ● Statistical analysis and data mining with R to identify trends and anomalies in maintenance data.

Transition of a French healthcare organisation's HIS (Hospital Information System) client/server solution to Web 2.0 (third level). ● Built a RESTful API using Node.js and Express, reducing server response time and improving data retrieval efficiency. ● Designed and implemented unit tests for all application components, achieving high code coverage and significantly improving the system's fault tolerance. ● Automation of unit testing processes using tools such as Jest and Selenium, optimizing the development cycle and reducing manual testing time. ● Use of Test-Driven Development (TDD), leading to increased code reliability and a reduction in bugs after deployment (Istanbul, SonarQube, Pair Programming). ● In-depth reviews of Pull Requests, providing accurate and constructive feedback to improve code quality and maintain development standards. ● Active participation in refactoring sessions to identify and resolve code pain points, improving the readability, maintainability, and performance of the system. ● Designed and optimized database schemas for MongoDB and SQL databases, ensuring efficient data storage and retrieval while improving overall application performance. ● Integrated various third-party APIs and services into the system using RESTful protocols and Postman for testing, enhancing the system’s functionality and interoperability with external platforms. ● Set up and maintained CI pipelines using tools such as Jenkins and SonarQube, automating the build, test, and deployment processes, which improved consistency and reliability in deployment workflows. ● Conducted regular code reviews and implemented security best practices such as input validation, authentication, and authorization, helping to mitigate vulnerabilities and ensure compliance with healthcare data security standards.