ML Engineer / Toronto

Intersog® is a Chicago-based provider of ROI-driven custom web and mobile development specializing in the delivery of full-service, end-to-end solutions, and project resources to Fortune 500 companies, SMEs, and startups. We help our clients attack their ambitious business goals, solve skills shortage issues, and become innovative by building Dedicated Software Development Teams in Mexico, Canada, the U.S.A., and Ukraine and/or providing on-demand IT project resources to complete required skills on their in-house teams.

About the role

We seek an ML Engineer to join the AI Practice team dedicated to pioneering AI-driven solutions, encompassing hyper-personalization, AI-powered selling strategies, extensive unstructured data analysis through NLP, and innovative dynamic pricing approaches. Utilizing the latest in machine learning and deep learning technologies alongside comprehensive engineering platforms, they are committed to developing impactful products and client-centered solutions.

Requirements

• Develop and manage ML model pipelines, focusing on feature engineering, model training, and inferencing.

• Scale ML algorithms for large data sets under strict service level agreements (SLAs).

• Enhance ML Engineering platforms and ensure the implementation of MLOps practices for model monitoring and feedback loops.
• Write clean, production-quality code that adheres to best practices and design guidelines.
• Work collaboratively with global teams to deliver projects, utilizing development and project management tools to maintain organization and communication.
• Engage in continuous learning to stay abreast of new technologies and methodologies in ML architecture and design.

Qualifications:

• Bachelor's or Master's in Computer Science, MIS, IT, or related fields.
• 2-4 years of experience in deploying production-level ML models.
• Proficiency in Python / PySpark and experience with ML platforms (e.g., Dataiku, Sagemaker, MLFlow).
• Skilled in deploying models to cloud services (AWS, Azure, GCP) and optimizing ML models for performance and scalability.

• Solid understanding of machine learning, deep learning fundamentals, common data structures, algorithms, and design patterns.
• Excellent communication skills, both verbal and written.