Mehdi Aissani

Technical Lead on the the Defense area / High-Assurance Engineering for Safety-Critical Systems 🔬 Context: Operated within a strictly regulated environment designing software solutions where security, traceability, and zero-failure are non-negotiable standards. Any other part of the context of this mission couldn't be revelated to the public due to a NDA. 📋 Core Responsibilities: - Technical Leadership: Guided the engineering strategy and fostered best practices within the development team. - Agile Orchestration: Led Scrum ceremonies and managed the delivery roadmap to align technical output with critical project milestones. - Architecture & Quality: Designed resilient software architectures while actively reducing technical debt to ensure long-term maintainability. - Performance & FinOps: Optimized code performance for critical paths and managed resource efficiency (FinOps). - Compliance: Enforced rigorous documentation and traceability standards required for defense-grade applications. ⚙️ Technical Ecosystem (Confidential): Due to the sensitive nature of this mission (NDA), the specific stack cannot be disclosed. - Context: Distributed Systems, High-Security Infrastructure. - Focus: Modern Backend Architecture, Containerization, and Strict CI/CD Gates.

High-Performance Computing for Financial Risk Modeling 🔬Context: Engineered next-generation calculation engines for the reinsurance sector, focusing on the acceleration of complex actuarial models (Longevity Swaps) through hybrid software solutions. 📋 Core Responsibilities: - HPC Architecture: Designed and developed high-performance computing applications for critical risk assessment and financial modeling. - Performance Engineering: Implemented hybrid architectures bridging Python and Rust to optimize calculation speed without sacrificing usability. - Library Development: Created custom, highly optimized libraries using PyO3 and Maturin to expose Rust performance to Python environments. - Distributed Collaboration: Orchestrated technical delivery and architecture decisions within a fully distributed engineering team. ⚙️ Technical Ecosystem: - Core Stack: Rust (Tokio, Futures), Python (Django). - Interoperability: PyO3, Maturin (FFI). - Focus: Low-latency Calculation, Asynchronous Programming.

High-Frequency Blockchain Ingestion & Distributed Infrastructure 🔬 Context: Architected the core ingestion engine for a next-generation decentralized marketplace (ARK Project) on Starknet, managing the real-time indexing and processing of massive on-chain event streams with high reliability. 📋 Core Responsibilities: - Event-Driven Architecture: Engineered a high-throughput ingestion system to track real-time Smart Contract interactions (ERC20, ERC721, ERC1155) with sub-second latency. - Multi-Cloud Resilience: Designed a fault-tolerant infrastructure spanning AWS and Scaleway, implementing Disaster Recovery protocols and automated failover strategies. - Performance Optimization: Optimized database interactions and log management using ClickHouse to handle high-velocity blockchain data. - DevSecOps Automation: Built comprehensive CI/CD pipelines via GitHub Actions, ensuring reproducible deployments and code quality enforcement across the stack. ⚙️ Technical Ecosystem: - Core Stack: Rust, Cairo (Smart Contracts), TypeScript. - Data & Observability: ClickHouse, PostgreSQL-HA, OpenTelemetry, Jaeger. - Infra: Kubernetes, Terraform, Multi-Cloud (AWS/Scaleway).

High-Availability Microservices for Financial Systems 🔬 Context: Designed and scaled critical backend components for the payment ecosystem of Europe's leading cloud provider, ensuring resilience and strict compliance for sensitive banking flows. 📋 Core Responsibilities: - Microservices Architecture: Engineered scalable Golang services to orchestrate complex payment processing and transaction workflows. - Advanced Observability: Implemented custom telemetry solutions using OpenTelemetry to monitor SLO/SLA compliance and guarantee system reliability. - Data Compliance: Designed automated mechanisms for GDPR compliance, managing secure data anonymization and deletion at scale. - Developer Experience: Created and maintained a private Golang SDK to standardize development practices and accelerate internal delivery. - Multi-Channel Ingestion: Developed a unified service to ingest and normalize disparate financial data streams (webhooks, banking files). ⚙️ Technical Ecosystem: - Core Stack: Golang, PostgreSQL, Redis. - Infrastructure: Kubernetes, CI/CD (Internal). - Focus: OpenTelemetry, OpsGenie, Distributed Tracing, FinOps & Compliance

Full Stack Architecture & Observability Strategy 🔬 Context: Enhanced operational efficiency for a major real estate management group by developing instrumented internal tools and designing robust monitoring frameworks to track user activity and system performance. 📋 Core Responsibilities: - Solution Architecture: Designed and developed critical internal applications using a modern TypeScript/Node.js stack. - Advanced Observability: Engineered custom metric tracking systems and configured DataDog ingestion pipelines for granular user analytics. - Frontend Engineering: Built responsive React interfaces integrated with backend analytics for real-time performance monitoring. ⚙️ Technical Ecosystem: - Core Stack: TypeScript, Node.js, React. - Infrastructure: AWS (ECS, ECR), Kubernetes, Docker. - Focus: DataDog (Observability), MongoDB, GitLab CI.

High-Frequency Trading System Engineering & Low-Latency Architecture 🔬 Context: Built a comprehensive High & Mid-Frequency Execution System from the ground up, leveraging Rust for the entire infrastructure to support Statistical Arbitrage strategies on digital assets. 📋 Core Responsibilities: - Core Execution Engine: Engineered the complete trading infrastructure in Rust, focusing on zero-cost abstractions to ensure minimal latency for High-Frequency execution. - Low-Latency Networking: Implemented ultra-fast internal communication using SharedRingBuffer (SHM), ZMQ, and UDP Multicast to optimize data distribution between microservices. - Algorithm Optimization: Collaborated on the debugging and technical refinement of Statistical Arbitrage algorithms to ensure stability and performance in production. - Cloud Infrastructure: Architected a hybrid and scalable infrastructure on AWS using ECS, Kubernetes (EKS), and S3 for storage. - Data Engineering: Deployed ClickHouse for high-performance analytics and Redis for hot-data caching. - Monitoring Interfaces: Developed real-time trading dashboards and system monitoring tools using React. ⚙️ Technical Ecosystem: - Core Stack: Rust, Python (Algo/Debug), React. - Low Latency: SharedRingBuffer, UDP Multicast, ZMQ. - Infrastructure: AWS (EC2, ECS, EKS, S3), Kubernetes, Docker. - Data: ClickHouse, Redis.

Lead Architect: OTC Trading Platform & Market Making Systems 🔬 Context: Led the design and development of a proprietary electronic OTC trading venue and real-time Market Making engines, ensuring high availability and scalability for institutional digital asset trading. 📋 Core Responsibilities: - Platform Architecture: Architected and built the OTC trading platform from scratch, handling real-time price streaming and execution via raw TCP and WebSockets. - Low-Latency Synchronization: Engineered high-performance inter-process communication using ZeroMQ (ZMQ) and Shared Memory to ensure precise component synchronization. - Intelligent Scaling: Designed a robust Kubernetes infrastructure featuring advanced autoscaling based on custom metrics, allowing the system to dynamically adapt resources to client load and market volatility. - Technical Leadership: Defined the technical roadmap and led the development team in delivering critical Market Making features. ⚙️ Technical Ecosystem: - Core Stack: Rust, Python, ZMQ. - Connectivity: TCP, WebSockets, REST. - Infrastructure: Kubernetes (HPA with Custom Metrics), Docker. - Focus: OTC Trading, Market Making, IPC (Shared Memory).

AIOps Platform Architecture & Predictive Monitoring 🔬 Context: Architected a next-generation IT monitoring platform designed to predict infrastructure incidents and eliminate alert fatigue. The goal was to accelerate resolution times through Machine Learning while ensuring high performance across multi-OS environments. 📋 Core Responsibilities: - Platform Architecture: Led the design and development of the core monitoring solution, enforcing strict coding standards, security improvements, and performance optimizations. - Predictive ML Engineering: Implemented Machine Learning models using TensorFlow and PyTorch to predict system failures (CPU, Memory, Disk) before they impact operations. - High-Performance Agents: Developed efficient, multi-OS data collection agents (Windows/Linux/Mac) using C++ and Golang to capture real-time system metrics and logs. - Streaming Pipeline: Engineered a robust ingestion pipeline using Kafka and Protobuf (gRPC) to handle massive telemetry data streams with low overhead. - Data Visualization & Storage: integrated the ELK Stack (ElasticSearch, Logstash, Kibana) for log aggregation and visualization. - Infrastructure Orchestration: Managed the containerized infrastructure on Kubernetes, utilizing Helm and Istio for service mesh management. - Quality Assurance: Implemented behavioral testing frameworks using go-dog to ensure system reliability. ⚙️ Technical Ecosystem: - Languages: Golang, Python, C++, Node.js. - ML & Data: TensorFlow, PyTorch, Kafka, ElasticSearch, Logstash, Kibana. - Infrastructure: Kubernetes, Docker, Istio, GitLab CI. - Protocols: gRPC, Protobuf.

High-Performance Marketplace & SEO Optimization 🔬 Context: Contributed to the scaling of a leading automotive marketplace by engineering high-performance frontend architectures designed for SEO efficiency and fast Core Web Vitals. 📋 Core Responsibilities: - Full Stack Development: Designed and implemented critical buy/sell landing pages using a modern React/Node.js stack. - SEO & Performance: Optimized application rendering (Next.js) and asset delivery strategy to maximize search engine visibility and user experience. - DevOps Engineering: Built custom CI pipelines and provisioned ephemeral End-to-End (E2E) testing environments to secure the deployment lifecycle. - Infrastructure Management: Managed cloud resources using AWS services (S3, CloudFront, EC2) and Puppet for configuration management. ⚙️ Technical Ecosystem: - Core Stack: React, Next.js, Node.js. - Infrastructure: AWS (S3, CloudFront, EC2), Puppet. - Focus: Technical SEO, Server-Side Rendering (SSR), CI/CD Automation.

Solar Data Ingestion, processing & Exploitation pipeline. 🔬 Context: EDF in its transformation towards clean energy. Construction of a monitoring system for the solar park. 📋 Core Responsibilities: - Design and construction of the solar park's data lake - Creation of a BI dashboard for real-time data visualization - backlog task breakdown and estimation - IoT sensor data integration ⚙️ Technical Ecosystem: - Backend: NodeJS, Python - Frontend: React, D3.js - Infrastructure: AWS (S3, Lambda, Route53, CloudFront ) - Databases : PostgreSQL, Redis

Search Engine conception, ingestion and Natural Language Processing 🔬 Context: Bouygues Telecom had a 3-month deadline to migrate their internal search engine. An on- premises solution was required for data security. 📋 Core Responsibilities: - Architecture and creation of the internal search engine solution on AWS - indexing and crawling system design with NLP - Project manager with demonstrations for company executives - Design of a highly scalable crawler and indexer - clustered ElasticSearch servers ⚙️ Technical Ecosystem: - Backend: NodeJS, Python (NLP / Phonemes ) - Frontend: React - Infrastructure: AWS (S3, Lambda, Route53, CloudFront ), Gitlab CI - Databases : PostgreSQL, Redis

Enable Archicad Software and Twinmotion application to have a Web VR Application. 🔬 Context: Abvent produces tools for architects: ArchiCAD, Twinmotion , Twinlinker . Development of high-availability 3D web applications. 📋 Core Responsibilities: - Design, architecture and development of Twinmotion & Twinlinker with AframeVR and Angular - Lambda NodeJS Backend Development (AWS) - microservice infrastructure for high availability - Infrastructure design using an IAC ( CloudFormation ) model ⚙️ Technical Ecosystem: - Backend: NodeJS - Frontend: Angular , AframeVR ( WebGL / WebVR ), Javascript - Infrastructure: AWS (S3, Lambda, Route53, CloudFront ), Gitlab CI - Databases : PostgreSQL, Redis