Job Description
What's the opportunity?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 s 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 Open Shift + 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.
Your responsibilities include:
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
Must Have:
5+ years of industry experience in software/platform engineering
Deep hands-on experience with Kubernetes in production (pod security, RBAC, storage classes, Cron Jobs, admission webhooks, custom controllers). Open Shift 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 (Git Hub Actions, Helm, Git Ops, 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
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
Own significant platform capabilities on a small team with high autonomy and direct business impact
Work with cutting-edge GPU hardware (NVIDIA B300, H100, A100) powering real ML research
Collaborate with high-performing engineers and AI researchers solving problems in finance
A comprehensive Total Rewards Program including bonuses and flexible…
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