Machine Learning Engineer, Computer Vision – Azure

Responsibilities

• Audit, clean, and improve training datasets to enhance model performance and reliability.
• Build and maintain robust, reproducible data and machine learning pipelines.
• Develop and execute model evaluation frameworks across different datasets, regions, and business scenarios.
• Analyze model performance and identify opportunities for optimization and improvement.
• Conduct machine learning experiments involving feature engineering, data augmentation, model architectures, and hyperparameter tuning.
• Apply computer vision and multimodal AI techniques to solve real-world business challenges.
• Integrate validated machine learning solutions into production environments.
• Support model deployment, testing, documentation, and ongoing maintenance activities.
• Manage and optimize machine learning training workflows within Azure-based environments.
• Perform SQL-based data analysis and provide actionable insights to stakeholders.
• Collaborate with engineering and data teams to improve development standards, automation, and reproducibility.

Requirements

• Bachelor's Degree in Computer Science, Information Technology, or a related field.
• 3+ years of solid experience in Machine Learning Engineering, Applied Machine Learning, or a related role.
• Strong Python programming skills with experience building production-grade applications and pipelines.
• Hands‑on experience with machine learning libraries such as scikit‑learn, NumPy, pandas, Polars, and PyArrow.
• Experience with deep learning frameworks such as PyTorch or equivalent.
• Strong SQL skills and experience performing data extraction and analysis.
• Experience working with Azure Machine Learning or similar cloud-based ML platforms.
• Familiarity with software development best practices, version control, code reviews, and pull request workflows.
• Strong written communication and technical documentation skills.
• Preferred: Experience with computer vision, multimodal AI models, transfer learning, or vision encoders such as CLIP or SigLIP.
• Familiarity with tools such as PyTorch Lightning, Optuna, and MLflow.
• Experience with CI/CD pipelines, containerization, and MLOps practices.
• Exposure to Databricks, Azure Blob Storage, or similar cloud data platforms.
• Ability to interpret and apply machine learning research to practical business use cases.
• Experience in consumer research, retail analytics, behavioral science, or related domains is an advantage.

Core Competencies

Demonstrates expertise in Machine Learning Engineering, focusing on building and maintaining data pipelines, model evaluation frameworks, and integrating machine learning solutions into production. Proficient in Python, SQL, and various machine learning libraries and frameworks, with a strong emphasis on optimizing model performance and applying advanced techniques in computer vision and multimodal AI.