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Work Experience

• Machine Learning Intern, Hydro-Québec Research Institute (IREQ)

  May, 2024 - Jan, 20258 months

  Collaborated with client stakeholders to automate electrical equipment monitoring using Vision-Language Models (VLM) and cloud-native architectures. Successfully passed Government of Quebec security screening.

  • Built a robust AI pipeline using Azure AI Services, Azure Functions, and Azure Cosmos DB for high-availability data storage and processing.

  • Translated complex business processes into technical requirements for an inspection robot integrated with ROS.

  • Created and executed comprehensive test plans for AI-based applications, focusing on functional reliability and performance under edge-case environmental conditions.

  • Collaborated with cross-functional teams of architects and engineers to ensure AI solutions integrated seamlessly into the existing technology stack.

  • Conducted data preprocessing and annotation for VLM fine-tuning, ensuring ethical data handling and high model accuracy.

• Machine Learning Engineer, MARSS Group

  Dec, 2022 - Jul, 20237 months

  Developed production-ready Object Detection models with a heavy focus on performance testing and optimization for edge deployment.

  • Implemented and tested one-stage CNN object detectors, achieving high-performance benchmarks on low-power edge devices.

  • Performed rigorous functional and performance testing to validate neural network compression techniques (pruning/quantization).

  • Worked independently with minimal supervision to deliver production-grade Computer Vision assets for real-world security use cases.

• Data Scientist Apprenticeship, AzurIA

  Sep, 2021 - Nov, 20221 year 2 months

  Supported R&D for a startup, managing the full ML lifecycle from literature survey to cloud deployment on GCP.

  • Deployed and managed ML workloads on GCP Compute Engine, optimizing resource allocation for large-scale multi-spectral image processing.

  • Built automated CI/CD pipelines (GitLab) that included automated AI testing for boat detection and cloud segmentation models.

  • Experimented with and tested state-of-the-art TinyML models on various edge hardware (Jetson, Raspberry Pi) to ensure cross-platform compatibility.

  • Collaborated with technical and non-technical stakeholders to define PoC goals for anomaly detection in manufacturing.

• Data Scientist Intern, Center Antoine Lacassagne, CHU Nice

  Jun, 2021 - Aug, 20212 months

  Developed and validated a machine learning model for medical diagnosis, leading to a peer-reviewed publication.

  • Validated RM (Risk of Malignancy) models using rigorous statistical testing and performance comparison of ML methods.

  • Published findings in the European Journal of Endocrinology, demonstrating ability to communicate technical results to the scientific community.

Skills

Education

Awards

Volunteer Work