Safouane Ennasser

Created and scaled an enterprise focused on AI-powered virtual assistants and chatbots. Designed, built, and marketed three SaaS products from MVP to production: Latcha-ai.com: real-time audio assistant helping candidates during video interviews Candi-date (in development): recruitment assistant supporting HR professionals during technical interviews Sphereastrale.com: AI-driven guidance platform, responsibly built with transparency and safeguards Key contributions Full product ownership from prototyping, LLM/ASR integration, and cloud deployment to production monitoring Market research, pricing studies, SEO, and go-to-market strategy Roadmap design, benchmarking, milestone planning, and performance tracking Compliance with GDPR, implementation of legal frameworks, and ethical AI safeguards Balanced workload with 40% technical development and 60% strategy, marketing, and operations Skills:

Data pipes and architecture: Migrating Datawarehouse Schema and implementing Facts and Dimensions Creating ETLs from multiple sources, including geospatial data. Scripts to enhance Airflow capabilities. Dockerization of existing Solutions. Maintenance and monitoring. Airflow, AWS, S3, RDS Redshift, Snowflake, dbt, Python, Docker, Warehouse, Star Schema Data analysis and investigation: Spotting and fixing data discrapencies Monitor solutions and logs Create Alerts and Dashboards ELK, Prometheus, Grafana, SQL Streaming and real time classification: Animate a cross-teams workshops to identify the needs. Design the architecture and deployment process. Implementing a real-time rule based classificator using Open source RedHat Drools. Create Business processes, triggers and actions. Dockerize and Deploy Kafka, Streaming, AWS, Docker, Drools, Scala, Akka Participating in : Implementing some features of a map loader Load and parse maps and confs Monitor and create dashboards OSM, Scala, AWS Implementing some features of a dispatcher Conf encoding and loading Update metrics and creating dashboard panels for monitoring. Scala ZIO, AWS

Implementing : * A rule-based Real time classifier for a huge streaming data. * A web ui to monitor, and update rules. * A synchroized archiving solution, to keep all data stream. * Stress testing for components . Env : Google Cloud, Spark, Kafka, Python, Docker,

Automating machine learning algorithm. Architecturing a full data pipeline for real time recommendation system. Web solution to schedule, monitor and evaluate machine learning algorithms. Upgrading tools and framwks Spark/Scala, Hadoop HDFS/MR, Hive, Hbase, Aws, Play framework, Mithril JS, PostgreSQL

Skills: Bases de données · Architecture · Leadership · JSON · Ingénierie

Skills: Bases de données · Architecture · Leadership · JSON · Ingénierie

Skills: Bases de données · Architecture · Leadership · Big data · JSON · Ingénierie