Senior ML Platform Engineer (AI Farm)

RBC • Toronto, ON

2d • Onsite

About The Position

We're looking for a Senior ML Platform Engineer to join the AI Farm team — RBC's enterprise GPU compute and data platform for machine learning. You'll own and deliver critical platform capabilities that enable hundreds of ML researchers and engineers to train models, access data, and deploy at scale. This isn't a typical MLOps role. You'll be building the platform itself — the Kubernetes infrastructure, data access layer, compliance automation, and developer tooling that our ML teams depend on daily. You'll work at the intersection of distributed systems, data engineering, and platform engineering, solving problems like multi-tenant GPU scheduling, data governance enforcement, and self-serve infrastructure provisioning. At RBC Borealis, you'll join a small, high-impact team that operates AI Farm — an on-premise OpenShift + Run:AI cluster with H100, B300, and A100 GPUs serving multiple business units. You'll have direct ownership over system design decisions and ship features that immediately impact researcher productivity.

Requirements

• 5+ years of industry experience in software/platform engineering
• Deep hands-on experience with Kubernetes in production (pod security, RBAC, storage classes, CronJobs, admission webhooks, custom controllers). OpenShift experience is a strong plus.
• Proficiency in Python for building production tools, automation scripts, CLIs, and libraries
• Experience operating distributed data systems (Trino/Presto/Spark, SQL engines, Iceberg/Hive catalogs, or similar)
• Strong CI/CD and automation skills (GitHub Actions, Helm, GitOps, infrastructure-as-code)
• Experience building multi-tenant platforms with self-serve provisioning for internal teams
• Ability to own and deliver complex, ambiguous projects end-to-end with minimal direction

Nice To Haves

• Experience with data governance, compliance automation, or security enforcement on shared platforms
• Hands-on Prometheus/Grafana: building dashboards, alerting, and instrumentation from scratch
• Container image lifecycle management (registries, scanning, enforcement policies)
• Experience with GPU compute platforms (Run:AI, Slurm, or cloud GPU scheduling)
• Familiarity with S3-compatible object storage and persistent volume management
• Experience with Trino/Starburst (resource groups, connectors, column masking, SEP)
• OPA/Gatekeeper policy-as-code experience
• Familiarity with ML workflows (training jobs, experiment tracking, model serving) — enough to empathize with platform users
• Experience in regulated industries (financial services, healthcare) with compliance requirements
• Strong fundamentals in networking, storage, and distributed systems

Responsibilities

• Designing and building Kubernetes-native automation for platform operations: PV lifecycle management, namespace provisioning, compliance scanning, and workload enforcement
• Owning the data infrastructure layer: Trino/Starburst cluster operations, column-level data masking, resource group management, and catalog provisioning automation
• Building developer-facing tools and libraries (Python SDK, CLI) that reduce cognitive load for ML teams accessing data and compute
• Implementing data governance and compliance systems: automated scanning, classification integration, retention enforcement, and audit reporting
• Designing and operating observability pipelines: Grafana dashboards for GPU utilization, developer experience metrics, pipeline throughput measurement, and compliance coverage
• Collaborating with INFRA, security, and compliance teams to design and enforce platform policies (OPA admission webhooks, image enforcement, access controls)
• Contributing to architecture decisions (ADRs) and owning end-to-end delivery of multi-sprint epics with cross-team dependencies

Benefits

• bonuses
• flexible benefits
• competitive compensation
• commissions
• stock options where applicable
• Leaders who support your development through coaching and managing opportunities
• Clear growth path: Senior Engineer → Staff Engineer, with increasing scope over platform architecture

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed