Senior AI Platform & MLOps Specialist

Join a new Montréal-based team and help build the platform foundations required to bring AI solutions into production reliably.

As part of the Energy Intelligence initiative, you will create the infrastructure, automation, observability, and governance needed to develop, evaluate, release, deploy, and operate models across cloud, on-premises, edge, and disconnected environments.

Embedded within the AI team, you will develop reusable, self-service platform capabilities for AI engineers and product development teams.

You will work closely with the Principal Azure Cloud Architect, software developers, cybersecurity teams, and responsible AI stakeholders to establish reliable and secure ways to manage models, prompts, artifacts, environments, and releases.

This role offers the opportunity to solve complex platform and deployment challenges while helping teams move from experimentation to dependable product operations.

You do not need prior experience with every environment or technology listed.

We value strong foundations in automation, software engineering, and MLOps, along with the ability to learn, collaborate across disciplines, and make progress in an evolving technical environment.

What you'll be doing


* Build and maintain automated pipelines for model development, evaluation, packaging, release, and deployment.


* Enable reliable model inference across cloud-hosted, containerized, on-premises, edge, and disconnected environments.


* Implement model registries, artifact management, lineage, versioning, and release controls to support traceability throughout the model lifecycle.


* Create reproducible development and evaluation environments that enable consistent testing and validation.


* Establish monitoring for model quality, latency, availability, resource consumption, and cost.


* Automate security scanning, dependency management, testing, and policy enforcement across AI development and deployment workflows.


* Implement controlled processes for promoting models and prompts across development, test, and production environments.


* Support GPU and CPU inference workloads and optimize performance, resource utilization, and cost.


* Develop rollback, fallback, and incident-response mechanisms to support reliable product operations.


* Collaborate with cybersecurity and responsible AI stakeholders to incorporate security, governance, and responsible AI requirements into the platform.


* Develop reusable, self-service platform capabilities that enable AI engineers and product development teams to build, evaluate, and deploy AI solutions more efficiently.

What you bring


* Experience with cloud platforms, containers, Kubernetes, CI/CD pipelines, and infrastructure as code.


* A background in deploying and operating machine-learning or AI systems.


* Proficiency in software development and automation.


* Knowledge of monitoring, reliability, security, and release management prac...