MLOps Engineer (Remote)

CDM Smith; Toronto; Remote; CAD 144,000 - 253,000; Full time

Job summary

A visionary tech company in Toronto is seeking an experienced MLOps Engineer to oversee the operational backbone for AI and Data Engineering products. This role involves designing scalable infrastructure, developing CI/CD pipelines for machine learning models, and collaborating with Data Scientists to ensure system reliability and performance. Required qualifications include a Bachelor's degree and at least 5 years of relevant experience. The position offers a competitive salary based on qualifications.

Qualifications

• 5 years of related experience.
• Experience with container orchestration (Kubernetes).
• Understanding of data storage paradigms.

Responsibilities

• Design and maintain infrastructure for machine learning lifecycle.
• Develop CI/CD pipelines for ML models.
• Collaborate with Data Scientists to deploy models.

Skills

Software engineering fundamentals

Containerization (Docker)

Cloud infrastructure (AWS, GCP, Azure)

Python

Communication skills

Education

Bachelor's Degree

Tools

Kubernetes

Terraform

MLFlow

Azure DevOps Pipelines

Job description

Why Trinnex?

If you are passionate about water and technology, Trinnex is the place for you! Trinnex is a visionary company that is transforming the way water resources are managed and protected. By combining cutting-edge digital technologies, such as sensor/IoT data, models, geospatial data, and AI/machine learning, we create innovative, smart, and scalable solutions that make a difference. Whether it's optimizing water supply and demand, detecting leaks and anomalies, or enhancing water quality and resilience, Trinnex delivers value and impact to public sector clients across the country.

Job Description

Trinnex, a wholly owned subsidiary of CDM Smith is seeking a MLOps Engineer with specialization in AI platform to join our growing team. Trinnex is building next generation tools that integrate sensor/IoT data, models, geospatial data and machine learning to solve unique engineering and environmental issues.

This position is based in Toronto, Ontario; candidates located in Vancouver, BC or Edmonton, Alberta may also be considered.

In this role, you will own the operational backbone for our AI and Data Engineering products. You will be responsible for the end-to-end production lifecycle of our ML models, from helping build the application services that wrap them to creating the automated systems for their deployment. Your ultimate goal is to ensure the overall health, scalability, and reliability of these machine learning systems in production. This requires close collaboration with internal resources to research and implement MLOps best practices, driving continuous improvement and automation across our platforms.

Responsibilities

• Design, build, and maintain scalable and reliable infrastructure to support the entire machine learning lifecycle, from experimentation and training to deployment and monitoring.
• Develop and manage robust CI/CD pipelines for ML models and associated software services, ensuring automated, high-quality releases.
• Collaborate closely with Data Scientists to containerize, deploy, and operationalize machine learning models, implementing solutions for both batch prediction and real-time inference use cases.
• Collaborate with teams to architect generative AI applications, providing expert guidance on connecting LLMs to proprietary data sources and enabling them to execute tasks on behalf of users.
• Champion MLOps best practices and empower the Data Science team by providing guidance, training, and support for new tools and automated workflows.
• Partner with Software Engineers to define and implement modern service architectures, including microservices and APIs, for ML-powered applications.
• Implement and manage cloud infrastructure using Infrastructure as Code (IaC) principles to ensure environments are reproducible, secure, and auditable.
• Establish and maintain comprehensive monitoring, logging, and alerting systems to track model performance, data drift, and infrastructure health, and aid in incident response.
• Work with cybersecurity and architecture teams to design and enforce security best practices across our cloud environment, including network configuration, identity management, and data protection.
• Maintain clear and detailed documentation for MLOps processes, infrastructure, and best practices.

Skills and Abilities

• Excellent software engineering fundamentals, with a solid understanding of modern software service architecture (e.g., microservices, APIs) and CI/CD principles.
• Deep, hands-on expertise with containerization (Docker) and container orchestration (Kubernetes).
• Proven experience designing, building, and securing infrastructure on a major cloud platform (e.g., GCP, AWS, Azure), with a firm grasp of core concepts like IAM and secure network architecture, including VPCs, firewall policies, and segmentation.
• Demonstrable understanding of the end-to-end machine learning lifecycle and experience deploying models for both batch and real-time/live inference workloads.
• Experience working with and understanding the trade-offs between different data storage paradigms, such as relational databases (e.g., PostgreSQL), analytical data warehouses (e.g., BigQuery), and cloud object storage (e.g., GCS, S3).
• Solid understanding of Python.
• Excellent communication, interpersonal, and organizational skills, with a demonstrated ability to manage and prioritize multiple tasks effectively, both independently and as part of a team.

LI-LP1 LI-REMOTE

Minimum Qualifications

• Bachelor's Degree.
• 5 years of related experience.
• Equivalent additional directly related experience will be considered in lieu of a degree.

Domestic and/or international travel may be required. The frequency of travel is contingent on specific duties, responsibilities, and the essential functions of the position, which may vary depending on workload and project demands.

Preferred Qualifications

• Professional experience with Google Cloud Platform (GCP), especially its AI/ML services like Vertex AI.
• Hands-on experience building applications that connect LLMs to external systems, such as using Retrieval-Augmented Generation (RAG) for querying data or enabling tool use (function calling). Familiarity with frameworks like LangChain is a plus.
• Experience with core MLOps components, including experiment tracking (e.g., MLFlow, Vertex AI Experiments) and model registries.
• Experience with modern workflow orchestration frameworks designed for machine learning (e.g., Kubeflow Pipelines, Flyte, or Prefect).
• Intermediate to advanced knowledge of Infrastructure as Code (IaC) tools, particularly Terraform.
• Experience managing Kubernetes applications using Helm.
• Experience with specific CI/CD tools (e.g., Azure DevOps Pipelines).
• Hands-on experience with service mesh technologies like Istio.
• Broader coding and debugging skills in languages such as JavaScript, C#, Java or Go.

Amount of Travel Required

0%

Background Check and Drug Testing Information

CDM Smith Inc. and its divisions and subsidiaries (hereafter collectively referred to as “CDM Smith”) reserves the right to require background checks including criminal, employment, education, licensure, etc. as well as credit and motor vehicle when applicable for certain positions. In addition, CDM Smith may conduct drug testing for designated positions. Background checks are conducted after an offer of employment has been made in the United States. The timing of when background checks will be conducted on candidates for positions outside the United States will vary based on country statutory law but in no case, will the background check precede an interview. CDM Smith will conduct interviews of qualified individuals prior to requesting a criminal background check, and no job application submitted prior to such interview shall inquire into an applicant's criminal history. If this position is subject to a background check for any convictions related to its responsibilities and requirements, employment will be contingent upon successful completion of a background investigation including criminal history. Criminal history will not automatically disqualify a candidate. In addition, during employment individuals may be required by CDM Smith or a CDM Smith client to successfully complete additional background checks, including motor vehicle record as well as drug testing.

Agency Disclaimer

All vendors must have a signed CDM Smith Placement Agreement from the CDM Smith Recruitment Center Manager to receive payment for your placement. Verbal or written commitments from any other member of the CDM Smith staff will not be considered binding terms. All unsolicited resumes sent to CDM Smith and any resume submitted to any employee outside of CDM Smith Recruiting Center Team (RCT) will be considered property of CDM Smith. CDM Smith will not be held liable to pay a placement fee.

Pay Range Minimum

$144,477

Pay Range Maximum

$252,824

Business Unit

TRX

Group

TXUP

Assignment Category

Fulltime-Regular

Employment Type

Regular

Visa Sponsorship Available

No - Please note that all applicants must be legally eligible to work in Canada, for the Company, at the time of hire. Furthermore, this is not a position for which the Company is offering immigration application sponsorship or support.

Accessibility

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