Amine OMRI

Leading the migration and modernization of a business-critical embedded software platform from on-premise infrastructure to AWS, transforming legacy Windows and Linux workloads into a scalable cloud-native architecture. Key achievements: • Designed and implemented AWS infrastructure using Terraform following Infrastructure as Code best practices. • Architected a hybrid ECS platform running both Windows and Linux containers. • Automated provisioning and configuration of Windows and Linux EC2 instances using PowerShell, Bash, and AWS CLI. • Implemented shared storage solutions using FSx and NFS-based architectures for application repositories and persistent workloads. • Designed secure networking architectures using VPCs, security groups, private endpoints, and IAM least-privilege access controls. • Integrated GitLab CI/CD pipelines for automated infrastructure deployment and application delivery. • Automated ECS cluster lifecycle management, bootstrap processes, and container deployment workflows. • Implemented S3-based artifact distribution and deployment mechanisms to replace legacy file-sharing solutions. • Produced architecture documentation, operational runbooks, and deployment standards for cloud operations teams. • Collaborated with development, infrastructure, and security teams to ensure compliance with enterprise cloud standards. Technologies: AWS (ECS, EC2, S3, FSx, IAM, VPC, SSM, CloudWatch), Terraform, GitLab CI/CD, Docker, PowerShell, Bash, Linux, Windows Server, NFS, SMB.

Led cloud transformation and modernization initiatives for automotive applications supporting crowdsourcing, HD mapping, cartography, and autonomous driving systems. Designed target cloud architectures and migrated legacy on-premise and embedded applications to AWS using rehost, replatform, and re-architect strategies. Key achievements: • Designed end-to-end cloud architectures spanning embedded vehicle software, cloud-native backend services, and large-scale data processing platforms. • Migrated multiple business-critical applications to AWS using ECS, EKS, Lambda, RDS, S3, SQS, SNS, API Gateway, and other cloud-native services. • Developed reusable Terraform modules and Infrastructure as Code frameworks to standardize infrastructure provisioning and deployment. • Designed Kubernetes platforms and Helm deployment frameworks for containerized workloads across multiple environments. • Built CI/CD pipelines using GitLab CI/CD and GitHub Actions, automating infrastructure deployment, application delivery, testing, and release processes. • Contributed to backend development using Go, building microservices, REST APIs, and event-driven services supporting cloud and embedded platforms. • Optimized cloud environments for scalability, reliability, observability, and cost efficiency. • Collaborated with software, embedded, cloud, and data engineering teams to integrate vehicle-generated data and cloud services for autonomous driving and mapping use cases. • Produced architecture diagrams, migration roadmaps, technical documentation, operational procedures, and cloud governance standards. • Conducted architecture reviews and implemented security best practices including IAM, network segmentation, encryption, and compliance controls. Technologies: AWS (EKS, ECS, Lambda, EC2, RDS, S3, SQS, SNS, API Gateway, IAM, CloudWatch, VPC), Terraform, Kubernetes, Helm, Docker, GitLab CI/CD, GitHub Actions, Go, Python, PostgreSQL, Microservices, Serverless, DevOps, Platform Engineering.

Contributed to the design and development of a cloud-native data platform enabling the collection, processing, semantic modeling, and consumption of IoT data at scale. The platform provided a unified abstraction layer over multiple heterogeneous data sources and exposed data through REST and GraphQL APIs for analytics, operational monitoring, and business applications Key achievements: - Developed cloud-native backend services enabling access to multiple data sources including Amazon S3, Athena, Timestream, MySQL, PostgreSQL, and MongoDB - Built scalable REST and GraphQL APIs providing a unified interface for querying and managing heterogeneous datasets - Developed data source connectors and abstraction layers supporting read/write operations, time-series data management, and metadata governance - Designed and automated ontology generation workflows to create semantic representations of IoT datasets and business entities - Implemented serverless event-driven architectures using AWS Lambda, S3, SNS, and SQS to automate data processing and ontology lifecycle management - Built ETL and data ingestion pipelines to collect, transform, validate, and enrich large volumes of operational and IoT data - Automated infrastructure provisioning and application deployments across multiple AWS environments following Infrastructure as Code and CI/CD best practices - Optimized platform reliability, scalability, and performance while supporting data-intensive workloads. - Implemented automated testing, code quality controls, type validation, and development standards to improve maintainability and delivery velocity - Participated in cloud architecture reviews, technical design discussions, and platform evolution initiatives Technologies: AWS (Lambda, S3, SNS, SQS, Athena, Timestream, IAM, CloudWatch), Python, FastAPI, Flask, GraphQL, Boto3, AWS SAM, PostgreSQL, MySQL, MongoDB, ETL, Data Engineering, CI/CD, GitLab CI, Pytest, Mypy, Pydantic, Moto, Black, Serverless Architecture

Mission: Improving a comment analysis platform using an artificial intelligence based solution. - Topic modeling: Data collection and pre-processing, Creation of embeddings using Sentence-Transformers and dimensionality reduction, Clustering and weighting using TF-IDF to extract the keywords that represent the topic of each cluster, Evaluation and optimization of results and deployment of the solution. - Sentiment Analysis: Using a labeled corpus to build a classification model, optimize it and deploy it using FastAPI. - Detection of similar concepts: Identification of identical concepts using Cosine similarity applied on embeddings. Used technologies: Pandas, Numpy, regex, emoji, Scikit-learn, UMAP, Xgboost, HDBSCAN, TF-IDF, Spacy, Sentence-Transformers, FastAPI, PyTorch

Mission: Improving Human Resource System using an AI-based solution. - OCR: Extract text data from input images using EASYOCR by finding the optimal rotation, cropping, and thresholding that allows better recognition. - Classification: Predict which candidates will change the company based on their data using several pre-processing approaches and multiple ML algorithms (XGBoost, AutoML, Stacking). - Resume-to-Job Matching: Create an automated resume matching using fastApi, ML and NLP technologies to calculate the similarity between resumes and job openings. Used technologies: pandas, numpy, GridSearchCV category-encoders, regex, xGBoost, scikit-learn, imbalanced-learn, SVM, CatBoostRegressor, Stacking, TPOTClassifier, EASYOCR, cv2, Fastapi, Sentence-transformers, Spacy, torch.

Creation of an internal, organizational and collaborative knowledge base using Microsoft SharePoint technology (O365)

The design and implementation of a Native Store Application for Android devices based on the WSO2 EMM (Enterprise Mobility Manager) platform.

Development of a mobile application allowing users located near 5 kilo meters to connect, communicate with each other, create events, share photos, log in and log out.

Development of a web application to manage the fleet of vehicles and employees of the Post Office, such as the fleet manager to manage vehicles, breakdowns, missions, oil change operations, fuel demand and drivers.