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
