Mid Data Scientist

Cloudfide engineers solutions where data becomes an accelerator for innovation. They build and evolve large-scale Big Data and cloud-native platforms for global clients, including leaders in retail and the Fortune 500. Their projects involve processing massive, complex datasets, operating fully in the cloud ecosystem, and working with AI, ML, MLOps, and modern data architectures. The company offers opportunities to shape solutions, mentor others, drive architecture decisions, and take ownership of strategic initiatives.

• 3+ years of commercial experience as a Data Scientist.
• Strong proficiency in Python and hands-on experience with machine learning and deep learning frameworks.
• Solid understanding of neural network architectures and model training best practices.
• Practical experience with computer vision techniques such as image classification, object detection, or image recognition.
• Familiarity with Azure ML, Databricks, or similar cloud-based ML platforms.
• Experience with LLMs, LangChain, or structured output techniques.
• Hands-on knowledge of OpenCV and image processing pipelines.
• Comfortable with Git and collaborative development workflows.
• Strong problem-solving skills and ability to work independently on technically challenging tasks.
• Fluent English communication skills.

Nice to have:

• Hands-on experience with Databricks Asset Bundles and MLOps workflows.
• Experience delivering end-to-end computer vision or image recognition projects in a commercial setting.
• Familiarity with Azure DevOps or CI/CD practices for ML pipelines.
• Exposure to structured output engineering for production LLM applications.
• Knowledge of model monitoring, drift detection, or A/B testing in production environments.

• Design and deliver production-grade machine learning and deep learning models using Python, Azure ML, and Databricks.
• Build and optimize computer vision and image recognition solutions using OpenCV and related frameworks.
• Work with LLMs, structured outputs, and LangChain to develop intelligent, data-driven applications.
• Manage end-to-end ML workflows including data preparation, model training, evaluation, deployment, and monitoring.
• Collaborate with data engineers, architects, and business stakeholders to translate complex requirements into scalable ML solutions.
• Manage code, experiments, and model versioning using Git and Databricks Asset Bundles.
• Contribute to MLOps practices to ensure models are reproducible, maintainable, and production-ready.
• Stay up to date with emerging research and technologies in AI, ML, and computer vision.