Senior Machine Learning Operations (MLOps) Engineer: AI/ML Google Cloud Platform

Location: Toronto, ON, Canada

Employment Type: Full-Time or Contractor

Work Model: Preferred Hybrid

Aithink is partnering with a leading technology company on an exciting opportunity for a Senior MLOps Engineer to architect and build the next generation of production-grade MLOps framework on GCP., supporting scalable ML systems and accelerating the path from experimentation to production.

You’ll join a talented, collaborative, and highly technical team and play a key role in shaping the architecture, automation, and engineering practices that enable machine learning at scale.

Key Responsibilities

• Architect and build production-grade MLOps framework on GCP, leveraging Vertex AI and cloud-native technologies.
• Design and operationalize end-to-end ML workflows for training, validation, model versioning, deployment, monitoring, and rollback.
• Build robust CI/CD and automation frameworks that enable repeatable, reliable, and efficient model delivery from experimentation to production.
• Develop and scale infrastructure using Kubernetes / GKE, Airflow, and related orchestration and platform technologies.
• Partner with Data Scientists and ML Engineers to productionize models and establish standardized, reusable deployment patterns.
• Engineer for scalability, reliability, observability, security, and performance across the ML lifecycle.
• Provide technical leadership and mentorship, influencing MLOps architecture, tooling, and engineering standards across teams.

Required Qualifications

• PhD/Master/Bachelor in computer science or engineering, or equivalent.
• 7+ years of hands-on experience in MLOps, ML Platform Engineering, DevOps, or a related discipline.
• 5+ years of experience in large-scale software development and production environments.
• Strong production experience with Google Cloud Platform (GCP) and cloud-native environments.
• Deep practical expertise with Vertex AI and production machine learning workflows.
• Strong experience with Kubernetes/GKE and containerized workloads.
• Hands-on experience with Airflow or similar workflow orchestration technologies.
• Strong understanding of CI/CD, infrastructure as code, automation, monitoring, and observability.
• Proven ability to design and deliver scalable ML infrastructure and production pipelines.
• Strong software engineering fundamentals, with experience applying them to ML systems and infrastructure.
• Ability to collaborate effectively with Data Scientists, ML Engineers, and Cloud/Platform teams.
• Strong technical ownership, problem-solving, and communication skills.

Nice to Have

• Experience with Pub/Sub, Flume, or streaming data architectures.
• Experience designing ML-specific CI/CD and deployment frameworks.
• Experience with model monitoring, automated retraining, and production rollback.
• Familiarity with MLOps maturity models, model governance, and enterprise ML practices.
• Experience operating large-scale, consumer-facing ML systems.
• Experience with high-availability or globally distributed ML environments.

Why Join?

• Build at scale Help architect and shape production MLOps framework supporting real-world ML applications at global scale.
• Technical ownership Influence architecture, tooling, standards, and the evolution of the MLOps environment.
• Talented team Work alongside experienced Data Scientists, ML Engineers, Cloud Engineers, and technical leaders.
• Modern technology Work with GCP, Vertex AI, Kubernetes/GKE, Airflow, CI/CD, and cloud-native technologies.
• Meaningful impact Enable ML teams to move from experimentation to reliable production faster and more efficiently.
• Strong engineering culture Join a friendly, collaborative, and technically strong team that values innovation, ownership, learning, and engineering excellence.

The Opportunity

This role is ideal for a Senior engineer who thrives at the intersection of machine learning, software engineering, cloud infrastructure, and production operations.

You’ll have the opportunity to solve complex production challenges, influence architectural decisions, and build the engineering practices that enable ML teams to deliver reliable, scalable systems in production.

If you’re excited about building a modern MLOps platform and making a meaningful impact on ML at scale, we’d love to hear from you.

How to Apply

Interested in building modern MLOps platform at scale?

We’d love to hear from you.

Apply through Aithink with…