Jobs in Ontario (212,502 jobs)

Jobs across Ontario, Canada. Choose a city or town to search nearby.

212,502 listings · 21,272 employers Counted Nov 8, 2026. How we count

About these jobs

Employers hiring include Loblaw Companies Limited (5,415 listings), Shoppers Drug Mart (4,203 listings), Walmart Canada (3,543 listings).

Common roles: Retail sales associate, Practical nurse, Registered nurse.

34,368 of these listings include employer-provided pay. Open a job to see its currency and pay period.

Advertised starting pay
Pay period	Median starting pay	Listings
CAD / hour	23.00	18,360
CAD / year	80,000.00	14,641
USD / year	155,000.00	86
CAD / month	4,054.00	68
CAD / week	1,500.00	47
CAD / day	101.00	23
USD / hour	20.00	13

Employer-listed minimums from at least 10 listings per currency and pay period. These are advertised starting amounts, not employee salaries. Jobs without a minimum and unusual amounts are excluded.

Workplace classifications: 0% remote, 17% hybrid, 82% on-site, 0% not specified. Hybrid and on-site classifications may be inferred from descriptions. Check the original ad.

Based on the dated count above. Listings can change. How we count

Jobs in Ontario

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Randstad Digital

Senior MLOps & Cloud Engineer (AWS / SageMaker)

Toronto, ON, Canada
Greater Toronto Area, Canada, Canada

Full-time

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Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Dec 6, 2026
Experience: Target benchmark of 7 years in tech/engineering, though strong candidates with 4–5+ years of relevant experience will be fully considered.
Posting language: English
Posted: Nov 6, 2026

Original job description

About our client

Our client is a premier financial services organization that has been helping Canadian investors build better financial futures for over 35 years. As a privately owned company, they boldly embrace innovation across their business while fostering a diverse, supportive, and inclusive culture.

Role Overview

This Senior MLOps & Cloud Engineer position exists to bridge machine learning model deployment, data transformation pipelines, and AWS cloud infrastructure. In this hands-on role, you will take complete ownership of promoting trained ML models into production, configuring batch and real-time inference endpoints, and automating cloud infrastructure across AWS and Snowflake environments. Within your first 3 to 6 months, you will take full technical accountability for end-to-end pipeline delivery, partnering directly with internal business teams and tracking project execution via Jira.

Main Responsibilities

Design, deploy, and operationalize production machine learning models, analytical solutions, and data pipelines on AWS.
Implement and manage end-to-end MLOps workflows using AWS SageMaker, handling model stitching, morning batch inference jobs, and real-time inference endpoints.
Build and automate robust Python and SQL-based data ingestion and transformation pipelines to supply clean data for model training and execution.
Manage and optimize cloud infrastructure, CI/CD pipelines, containerized workloads (such as ECS, EKS, or Snowflake Container Services), and operational runbooks.
Monitor model performance metrics in production to initiate retraining schedules and ensure high solution reliability.
Collaborate directly with internal business partners, Data Scientists, and Machine Learning Analysts to translate requirements into production implementations without requiring dedicated project managers.

Must-have required skills and experience

Education & Career Experience: Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related quantitative field (or equivalent practical experience). Target benchmark of 7 years in tech/engineering, though strong candidates with 4–5+ years of relevant experience will be fully considered.
Core Programming Languages: Strong hands-on proficiency in Python and SQL for data processing, model stitching, and pipeline automation.
MLOps & ML Platform: Proven hands-on experience deploying and operating machine learning pipelines on AWS, with heavy utilization of AWS SageMaker.
Cloud Infrastructure & DevOps: Demonstrated experience with Git, CI/CD automation, containerization (Docker, ECS, or EKS), logging, and cloud infrastructure management.
ML Algorithms: Solid understanding of traditional ML models (clustering, classification, forecasting, XGBoost, CatBoost, ensemble models) and how to evaluate and promote them into production environments.
Communication & Delivery Ownership: Excellent stakeholder management and communication skills, with a track record of partnering directly with business units and self-managing project timelines using Jira.

Nice-to-have skills

Emerging AI Frameworks: Practical experience with AgentOps / LLMOps / AIOps, Generative AI, retrieval-augmented generation (RAG), vector search, or AI Agent implementations.
Snowflake Ecosystem: Exposure to Snowflake data platforms or Snowflake Container Services.
ETL & Data Tools: Familiarity with Informatica Cloud, Databricks, Oracle, Azure Data Factory, Talend, or AWS Glue.
AWS Services & Certifications: Experience with Amazon Bedrock, Lambda, S3, or CloudWatch, or holding relevant AWS/DevOps certifications.

Logistics & Compensation

Location/Work Mode: Flexible Hybrid (2 days/week in-office). Work location is currently in downtown Toronto until late 2026, transitioning to Mississauga. Active Canadian work authorization is required.
Employment Type: Full-time permanent position.
Compensation: Up to $130,000 base salary + 20% annual performance bonus

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More about this job

Responsibilities

Deploy and operate production machine learning models, SageMaker workflows, batch and real-time inference, and Python- and SQL-based data pipelines on AWS. Manage cloud infrastructure, CI/CD, containerized workloads, and production model monitoring while coordinating delivery with business partners and data science teams.

Requirements

A bachelor's degree in a relevant field or equivalent practical experience is required, along with strong Python and SQL skills and hands-on experience deploying machine learning pipelines on AWS SageMaker. Candidates should have experience with cloud infrastructure, CI/CD, containers, traditional ML algorithms, and direct stakeholder collaboration; the target is seven years of experience, with candidates having four to five or more relevant years also considered.

Skills

Python
SQL
AWS SageMaker
MLOps
Machine Learning
AWS Cloud Infrastructure
CI/CD
Git
Docker
ECS
EKS
Snowflake
Data Pipelines
Model Deployment
Jira
Stakeholder Management

Visa sponsorship

Not detected in the job text

Keywords

AWS
AWS SageMaker
Snowflake
Python
SQL
MLOps
Machine Learning
Model Deployment
Batch Inference
Real-Time Inference
Data Ingestion
Data Transformation
Data Pipelines
CI/CD
Git
Docker
ECS
EKS
Snowflake Container Services
Jira
Clustering
Classification
Forecasting
XGBoost
CatBoost
Generative AI
RAG
Vector Search
AWS Lambda
Amazon S3

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