Hazem Aljene

Hazem Aljene Tunis, Tunisia --linkedin.com/in/hazemaljene/ PROFILE Machine Learning Engineer with over three years of experience specializing in deploying cutting-edge deep learning and machine learning solutions. Proficient in Python and MLOps, with hands-on expertise in developing scalable AI systems using cloud technologies such as AWS. Skilled in building end-to-end machine learning pipelines. Adept at collaborating with cross-functional teams to deliver personalized and impactful solutions that align with business priorities. Passionate about advancing machine learning research and applying innovative techniques to enhance customer experiences. TECHNICAL SKILLS ● Programming Languages: Python, Linux Shell, Javascript, C++. ● Machine Learning Frameworks: TensorFlow, PyTorch, LangChain. ● MLOps & Cloud Technologies: Docker, GitLab CI/CD, Kubernetes, MLflow, Apache Airflow, AWS (S3, EC2, EKS, SageMaker). ● Databases & Data Engineering: PostgreSQL, MongoDB, ClickHouse. ● Data Manipulation & Visualization: Pandas, Pyspark, Seaborn, Matplotlib, Apache Superset. ● Project Management: Jira, Notion. ● Git: Bitbucket, Gitlab, Github. ● Soft Skills: Problem-Solving, Adaptability, Critical Thinking, Creativity, Collaboration. LANGUAGES ● Arabic (native) English (fluent) French (advanced) SELECTED WORK EXPERIENCE DATAGRAM – TUNISIA since 10/2021 Machine Learning Engineer ● Analyzed and processed large-scale textual and visual data, enhancing the accuracy of predictive models through advanced preprocessing techniques. ● Designed and deployed predictive models using Deep Learning architectures (e.g., BERT, Attention Seq2Seq) and Machine Learning techniques, achieving a 97% accuracy in predicting product attributes from web-scraped data. ● Built and integrated a client-facing chatbot leveraging GPT and Llama models with LangChain, improving client satisfaction by delivering accurate and secure media performance responses. ● Deployed scalable, real-time chatbot API endpoints using FastAPI, ensuring secure and efficient interactions. ● Created a robust MLOps pipeline using Docker and GitLab CI/CD, automating model deployment and achieving 100% prediction coverage while reducing delivery times. ● Implemented MLflow to monitor the machine learning lifecycle and deployed interactive dashboards with Superset and ClickHouse, tracking prediction accuracy and coverage. ● Collaborated with cross-functional teams to develop data pipelines and optimize models, seamlessly integrating machine learning solutions into existing infrastructure. ● Technologies: Python (TensorFlow, PyTorch, Pandas), LLM, Hugging Face, LangChain, FastAPI, Docker, AWS (EC2, S3, SageMaker), Airflow, MLflow, PostgreSQL, MongoDB, GitLab, Superset. KNSD-SA – TUNISIA 02/2020 – 08/2020 Artificial Intelligence Intern ● Built a comprehensive database of Tunisian face images by implementing advanced web scraping techniques across various social media platforms, ensuring a diverse and high-quality dataset. ● Developed automated scripts for detecting, cropping, and selecting images, preparing the data for seamless integration into machine learning pipelines. ● Conducted an in-depth exploration of state-of-the-art GAN architectures, identifying the most suitable model for generating high-quality synthetic images. ● Applied transfer learning on StyleGAN2 to develop a custom image generator, achieving realistic and culturally representative synthetic face images. CRON SOLUTION – TUNISIA 07/2019 – 08/2019 Artificial Intelligence Intern ● Analyzed and processed Tarmed (Swiss Medical Tariff) invoice data to extract meaningful insights and prepare the data for predictive modeling. Applied advanced data cleaning and transformation techniques to handle inconsistencies and ensure high-quality input for model training. ● Designed and implemented a Recurrent Neural Network (RNN) model to predict the most suitable billing articles for each patient based on historical data. The model utilized sequential learning to capture temporal dependencies and improve prediction accuracy. ● Developed a user-friendly desktop application to integrate and deploy the RNN model, enabling seamless interaction for medical billing professionals. The application streamlined the prediction workflow, allowing users to input patient details and receive real-time billing article recommendations. EDUCATION National Engineering School of Carthage – TUNISIA 09/2017 – 07/2020 Mechatronic Engineering Degree Faculty of Sciences of Tunis - TUNISIA 09/2015 – 06/2017 Pre-engineering Class VOLUNTEERING AND EXTRACURRICULAR ACTIVITIES ● Data Co-Lab – TUNISIA o Worked on various research projects and with different teams. o Conducted workshops for students in different schools. ● IEEE ENICarthage Student Branch – TUNISIA o Designed and managed engaging content for the IEEE Student Branch's social media platforms. o Developed communication strategies and promoted branch events.