Senior MLOps & Cloud Engineer (AWS / SageMaker)

Randstad Digital; Toronto; Híbrido; CAD 117.000 - 143.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model

Descripción de la vacante

Randstad Digital in Canada seeks a Senior MLOps & Cloud Engineer to bridge ML model deployment, data pipelines, and AWS cloud infrastructure. You will own end-to-end pipeline delivery, configure batch and real-time inferences, and automate infrastructure across AWS and Snowflake environments.

You will lead production ML initiatives, collaborate with Data Scientists and ML Analysts, and track progress using Jira.

Formación

• Education: Bachelor's degree in Computer Science, Software Engineering, Information Technology, or related field; 7 years in tech/engineering, or 4–5+ years if strong.
• Core programming: Python and SQL for data processing, model stitching, and automation.
• MLOps & ML platform: Deploying and operating ML pipelines on AWS with SageMaker.
• Cloud Infra & DevOps: Git, CI/CD, containerization (Docker, ECS, EKS), logging, and cloud infra management.
• ML Algorithms: Knowledge of clustering, classification, forecasting, XGBoost, CatBoost, ensemble models, production promotion.
• Communication & Delivery Ownership: Stakeholder management, Jira-driven timelines.

Responsabilidades

• Design, deploy, and operationalize production ML models, analytics, and data pipelines on AWS.
• Implement and manage end-to-end MLOps workflows using AWS SageMaker, including batch and real-time inference endpoints.
• Build and automate Python and SQL data ingestion and transformation pipelines for model training.
• Manage and optimize cloud infrastructure, CI/CD pipelines, containerized workloads (ECS, EKS), and runbooks.
• Monitor production model performance and retraining schedules for reliability.
• Collaborate with Data Scientists and ML Analysts to translate requirements into production implementations.

Conocimientos

Python

SQL

Jira

Git

CI/CD

Communication

Educación

Bachelor's degree in Computer Science, Software Engineering, Information Technology, or related field

Herramientas

AWS SageMaker

Docker

ECS

EKS

Snowflake Container Services

Snowflake

Databricks

Informatica Cloud

Azure Data Factory

Descripción del empleo

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.
• Compensation: Up to $130,000 base salary + 20% annual performance bonus