Staff AI/ML Engineer

RBC•Calgary, AB

7d•Onsite

About The Position

We're looking for a seasoned Staff AI/ML Engineer to join the RBC Borealis AI Platform team. In this role you will own the end-to-end lifecycle of machine learning systems—from experimentation and validation all the way to high-throughput production serving. You will be the technical anchor for model operationalization at scale, setting the bar for reliability, observability, and engineering excellence across our AI platform. This is a rare opportunity to shape the foundation on which Canada's largest financial institution runs its most critical AI workloads. At RBC Borealis, you’ll be joining a team that works directly with leading researchers in machine learning, has access to rich and massive datasets, and offers the computational resources to support ongoing development in areas such as reinforcement learning, unsupervised learning and computer vision. You can find out more about our research areas at rbcborealis.com.

Requirements

Strong, production-proven experience with ML model serving and lifecycle management using SageMaker, MLflow, or comparable platforms.
Expert-level Python skills for backend service development, ML pipeline engineering, and automation scripting.
Deep hands-on experience with Apache Kafka and streaming/event-driven architectures for real-time feature pipelines and model inference.
In-depth knowledge of OpenShift Container Platform (OCP4) / Kubernetes for deploying and operating containerized ML workloads.
Proven experience building and maintaining CI/CD pipelines with GitHub Actions or equivalent tools for ML model delivery.
Hands-on expertise with observability platforms such as Datadog, Dynatrace, or Prometheus applied to distributed ML systems.
Demonstrated ability to design scalable distributed backend systems that operate reliably under high load in hybrid cloud environments (AWS / Azure / on-prem).
Experience with site reliability practices: SLOs/SLIs, alerting, incident management, and capacity planning for ML services.

Nice To Haves

Proficiency with MongoDB in production environments for storing model metadata, feature stores, or application state.
Experience with Elasticsearch for log aggregation, search, and ML-adjacent analytics use cases.
Familiarity with JavaScript or Go for building lightweight platform tooling or internal developer portals.
Background in audio processing pipelines—speech recognition, audio feature extraction, or real-time audio streaming—for multimodal AI applications.
Exposure to agentic AI systems, LLM orchestration frameworks, or self-hosted large language model infrastructure.

Responsibilities

Designing, building, and operating scalable ML model-serving infrastructure using SageMaker, MLflow, or equivalent platforms, ensuring low-latency, high-throughput inference in production—without involvement in upstream model training.
Architecting and maintaining real-time data and feature pipelines using Kafka and streaming frameworks to support online model serving and event-driven ML workflows.
Developing and maintaining robust backend services in Python that expose ML capabilities to downstream consumers via reliable, well-documented APIs.
Owning containerized deployment of ML workloads on OpenShift Container Platform (OCP4) / Kubernetes, including resource optimization, autoscaling, and rollout strategies.
Building and maintaining CI/CD pipelines (GitHub Actions) for model validation, packaging, and deployment, embedding quality gates and automated testing throughout.
Instrumenting ML services with comprehensive observability—metrics, logs, and traces—using Datadog, Dynatrace, Prometheus, or equivalent tooling; driving incident response and blameless post-mortems