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 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. 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) to 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, 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 Strong Preference: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 Nice to Have: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 What's in it for you?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 benefits, competitive compensation, commissions, and stock options where applicable Leaders who support your development through coaching and managing opportunities Clear growth path: Senior Engineer to Staff Engineer, with increasing scope over platform architecture About RBC BorealisRBC Borealis is the driving force behind Royal Bank of Canada's AI and data innovation. As part of Canada's largest financial institution, we bring together a team of architects, engineers, scientists, and product experts on a mission to revolutionize finance through world‑class research, solutions, and a resilient data platform. With locations across Toronto, Waterloo, Montreal, Calgary, and Vancouver, we are at the forefront of AI research and platform development. With a focus on cutting‑edge research in areas like time series forecasting, causal machine learning, and responsible AI, we are seamlessly integrating AI research and data engineering, to solve critical challenges in the financial industry. We are building intelligent, and scalable, data‑driven solutions that will help communities thrive and drive innovation for our customers across the bank. Inclusion and Equal Opportunity EmploymentRBC is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veterans status, Aboriginal/Native American status or any other legally‑protected factors. Disability‑related accommodations during the application process are available upon request.#J-18808-Ljbffr