New Value Solutions, a national IT consulting company, is seeking a MLOps Engineer to assist with deploying and maintaining analytical and machine learning models using Azure Machine Learning across environments. The role will require working closely with cross-functional teams including Data Scientists, Data Engineers, Cloud Ops Engineers, and Solution Architects, to integrate MLOps to design, build, maintain, and implement scalable machine learning pipelines and deployment solutions that create robust, secure, and production-ready analytical tools.

Responsibilities will also include the adoption of MLOps best practices, including unit test, drift detection and monitoring capabilities, and implementing, optimizing, and maintaining ML models within Azure DevOps, Azure Machine Learning, and related cloud infrastructure.

Responsibilities

• Deploy analytical and machine learning models into production using Azure Machine Learning and implement Azure ML pipelines across environments.
• Integrate MLOps within the end-to-end DevOps lifecycle, applying software engineering rigor and best practices to machine learning, including CI/CD and automation.
• Develop and deploy scalable tools and services to handle machine learning training and inference (batch and real-time), integrating models into existing data infrastructure.
• Maintain models and incorporate improvements without impacting system performance, while identifying and evaluating new techniques to improve performance, maintainability, and reliability.
• Provide in-hours and out-of-hours support for production processes, including alert automation for production failures, model accuracy drift, etc.
• Perform triage of alerts and root cause analysis for routing to appropriate teams (Data Scientists, Solution Engineers, Data Engineers, Data Stewards & support teams).
• Liaise with Cloud Ops to improve ML settings, architecture design, and platform optimization.
• Perform cloud cost & value estimation, monitoring, effectiveness/efficiency assessment, and controls budget estimation.
• Lead requirements gathering, design, and implementation for machine & deep learning production/solution releases.

Requirements

• 5+ years of work experience in machine learning operations, platform engineering, and/or DevOps.
• Experience building, maintaining, and enhancing end-to-end systems as a MLOps Engineer, Platform Engineer, and/or DevOps Engineer.
• Demonstrated experience leading ML/analytics development projects in Azure environments.
• Strong knowledge of Microsoft Azure ML, MLOps, DevOps, Python, SQL, and ETL processes.
• Ability to build Azure ML pipelines, understand tools used by data scientists, and decompose business requirements into detailed design specifications and code.
• Ability to build frameworks and automation scripts to accelerate development of common coding patterns.
• Experience investigating, diagnosing, and fixing production issues to meet service level agreement targets, independently completing root-cause analysis and impact assessment, and completing post-event preventative actions.

Priority Experience:

• Experience with Agile methodologies in a machine learning environment, including continuous integration/deployment, MLOps, and model governance initiatives.
• Experience with independent productionization and gate-keeping processes for machine learning models is expected.
• Strong communication skills with the ability to work collaboratively across teams and provide guidance on best MLOps practices.
• Ability to effectively manage and validate cloud costs and provide value estimation for machine learning initiatives.
• Experience setting up monitoring systems for model accuracy, drift detection, and production failures.

If you have the necessary expertise and are able to work in Canada, please submit your resume. While we thank all candidates in advance for their application, only those shortlisted will be contacted.

ID# 5015

The hourly rate range for this position is $70 - $95, with the final rate based on consultant experience and fit for the role.