Machine Learning Engineer - Senior

TekStaff IT Solutions; Toronto; Híbrido; CAD 120.000 - 148.000; Jornada completa

Descripción de la vacante

TekStaff IT Solutions is seeking a Senior Machine Learning Engineer for a 6-month contract based in Toronto, ON. The role involves a hybrid work arrangement with 3 days onsite and 2 days remote, a 7.25 hour workday, and mandatory screening checks.

The candidate will deploy, monitor, and operationalize AI/ML solutions using modern MLOps practices, building scalable models and reusable deployment patterns to drive business value.

Formación

• Advanced hands-on experience with Azure Machine Learning, MLOps, CI/CD, containers, APIs, automation, model deployment, monitoring and observability.
• Strong Python and ML engineering skills with demonstrated experience taking models from experimentation into reliable production environments.
• Background in statistics and computer programming.

Responsabilidades

• Deploy and monitor machine learning models in production using MLOps practices.
• Create reusable deployment patterns, tooling, and monitoring to move AI solutions from development to production.
• Collaborate with teams to translate business objectives into measurable ML solutions.

Conocimientos

Azure Machine Learning

MLOps

Python

Databricks

CI/CD

Herramientas

VSCode

Git

Jira

Azure ML Studio

Descripción del empleo

TekStaff's Client has a current vacancy for \" Machine Learning Engineer - Senior”

Job Description

This is a 6 month contract located in Toronto, ON (3 days onsite/2 days remote (subject to change).

Please note - This is a 7.25 hour work day.

*All successful candidates will be subject to a mandatory credit AND criminal check as per the client.

*Please note that all contractors are subject to a possible Holiday Leave program schedule, set by the client.

The Senior Machine Learning Engineer will deploy, integrate, monitor, and operationalize AI and machine-learning solutions using modern MLOps practices. The role will establish reusable deployment patterns, tooling, monitoring, and automation that enable AI solutions to move reliably from development into production and scale across the enterprise. Responsibilities: Creates machine learning models and utilizes data to train models Focuses on analyzing data to find relations between the input and the desired output Understands business objectives and develops models that help achieve them, along with metrics to track their progress Designs and develops machine learning and deep learning systems Runs machine learning tests and experiments Implements appropriate machine learning algorithms General Skills: Experience managing available resources such as hardware, data, and personnel so that deadlines are met Experience analyzing the machine learning algorithms that could be used to solve a given problem and ranking them by their success probability Experience exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world Experience verifying data quality, and/or ensuring it via data cleaning Experience supervising the data acquisition process if more data is needed Experience finding available datasets online that could be used for training Experience defining validation strategies Experience defining the preprocessing or feature engineering to be done on a given dataset Background in statistics and computer programming A team player with a track record for meeting deadlines, managing competing priorities and client relationship management experience

Must Haves:

• Advanced hands-on experience with Azure Machine Learning, Microsoft Foundry/Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, model deployment, monitoring and observability.
• Strong Python and machine-learning engineering skills with demonstrated experience taking models from experimentation into reliable production environments. Tech stack: Python, SQL, Power BI, Power Automate, VSCode, Git, Jira, Azure (ML Studio, AI Foundry), Databricks.
• Deep understanding of ML algorithms and statistics, including classification, regression, clustering, feature engineering, model evaluation, hyperparameter tuning, drift, retraining, performance optimization and production model troubleshooting.

TekStaff may use artificial intelligence (AI) tools as part of the applicant screening process. However, applications will also be reviewed by a member of our Recruitment team to ensure a fair and thorough assessment.