AI/ML Engineer - Remote / Telecommute

Cynet systems Inc; Remote; CAD 100,000 - 130,000; Full time

Job summary

A leading tech company in Canada is seeking a machine learning expert with over 5 years of hands-on experience. The candidate will design, train, and validate models to enhance process stability in concrete batching operations. Applicants must be proficient in Python and familiar with ML pipelines and various cloud services, particularly Azure and AWS. Responsibilities include leading data exploration, feature engineering, and model deployment. This position is critical for continuous performance improvement in machine learning applications.

Qualifications

• 5+ years of hands-on experience in applied machine learning.
• Proven experience in production-grade ML pipelines.
• Strong grasp of data science concepts.

Responsibilities

• Design, train, and validate machine learning models.
• Lead model development from data exploration to deployment.
• Define and implement retraining and validation strategies.

Skills

Applied machine learning

Python proficiency

Data science concepts

ML pipelines

CI/CD workflows

Cloud ML ecosystems

Tools

Azure ML

AWS SageMaker

Kubernetes

CosmosDB

Job description

Job Description:

Requirements:

• 5+ years of hands‑on experience in applied machine learning, with a focus on regression, forecasting, or optimization.
• Proven experience in production‑grade ML pipelines, from experimentation to deployment.
• Strong grasp of data science concepts such as cross‑validation, quantile modeling, and model safeguard techniques.
• Strong background in Python and data science libraries, with the ability to write clean, efficient, and production‑ready code.
• Experience on Git and related workflows, code review processes, automated testing, and CI/CD pipelines.
• Solid understanding of data lifecycle management, including time‑series feature engineering and retraining strategies.
• Experience with ML model monitoring, versioning, and continuous retraining frameworks.
• Familiarity with cloud ML ecosystems (Azure or AWS).
• Experience with Azure ML, CosmosDB, Service Bus, and Kubernetes.
• Experience with AWS SageMaker, ECS Fargate, SQS/EventBridge, and DocumentDB.

Responsibilities:

• Design, train, and validate machine learning models that improve process performance and stability in concrete batching operations.
• Lead end‑to‑end model development, from data exploration and feature engineering to deployment and validation.
• Define and implement retraining and validation strategies that ensure continuous performance improvement.
• Propose data selection and quality control methods to improve training representativity.