AI/ML Engineer, Computer Vision

Responsibilities

• Review, validate, and enhance training datasets to improve model accuracy and consistency.
• Design, build, and maintain scalable machine learning and data processing workflows.
• Create evaluation methodologies to assess model performance across multiple markets, datasets, and business contexts.
• Monitor model outcomes and recommend enhancements to improve effectiveness and efficiency.
• Run experiments involving feature development, model selection, hyperparameter optimization, and data enrichment techniques.
• Develop AI and computer vision solutions that address complex business requirements.
• Deploy and operationalize machine learning models within production environments.
• Support testing, monitoring, documentation, and continuous improvement of deployed models.
• Manage machine learning workloads and training processes within cloud-based platforms, primarily Azure.
• Utilize SQL to analyze large datasets and generate meaningful insights.
• Work closely with engineering teams to improve automation, reproducibility, and development best practices.

Qualifications

• Bachelor's Degree in Computer Science, Information Technology, Data Science, or a related field.
• At least 3 years of experience in Machine Learning Engineering, AI Engineering, or a similar technical role.
• Strong proficiency in Python and experience developing scalable applications and data pipelines.
• Hands-on experience with machine learning and data processing libraries such as scikit-learn, NumPy, pandas, Polars, and PyArrow.
• Practical experience with deep learning frameworks, preferably PyTorch.
• Strong SQL knowledge with the ability to perform complex data analysis and extraction.
• Experience using Azure Machine Learning or comparable cloud-based machine learning services.
• Understanding of software engineering fundamentals, including source control, code reviews, and collaborative development practices.
• Strong communication skills with the ability to create clear technical documentation.
• Preferred: Background in computer vision, multimodal AI, transfer learning, or vision-language models such as CLIP and SigLIP.
• Experience using tools such as MLflow, Optuna, and PyTorch Lightning.
• Familiarity with MLOps concepts, CI/CD processes, containerized deployments, and automation workflows.
• Exposure to Databricks, Azure Blob Storage, or similar cloud data technologies.
• Ability to translate emerging machine learning research into practical business applications.
• Prior experience in retail analytics, consumer insights, market research, behavioral science, or related industries is a plus.