Senior Mlops & Cloud Engineer

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 OverviewThis
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 ResponsibilitiesDesign, 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 experienceEducation & 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 skillsEmerging 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 & CompensationLocation/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
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