As a Machine Learning Engineer with 5 years of experience, I’ve contributed end-to-end to the productionization of multiple AI products at Ubisoft, GitGuardian, and Sanofi.

I’ve worked on complex use cases such as:

• E-commerce fraud detection using XGBoost, advanced feature engineering, MLOps tooling, AWS, and Kubernetes.
• Deployment of state-of-the-art NLP models for Secrets Detection (credentials) in source code. Stack: Transformers, PyTorch, FastAPI, ONNX Runtime, AWS EKS.
• Development of a full Terraform module for an Unstructured Data Pipeline, turning PDFs, PPTX, DOCX into vector embeddings in Pinecone. We used Weave to optimise and monitor the Pipeline. Stack: AWS Lambda, S3, ECR, Step Functions, Claude Sonnet, Amazon Nova Pro, Docling, HuggingFace models, AWS Textract, PyMuPDF, Pinecone, Weave

Programming language: Python

Machine Learning: ML, NLP, GenAI, Pytorch, Tensorflow, Scikit-Learn

Generative AI: OpenAI API, AWs Bedrock, HuggingFace, Langchain

DevOps: AWS, Kubernetes, Docker, Gitlab CI, Github Actions, Helm, Argo CD, Terraform

MLOps: DVC, SkyPilot, Okteto, BentoML, ClearML, Mlflow

Dataviz: Streamlit, Grafana, Tableau

Data Engineering: Dagster, Airflow, Spark, Hadoop (HDFS, Hive), Snowflake

And Team Work, Being friendly with colleagues and Goal oriented 😄

Please contact me through Linkedin, Malt or email.