RBC Borealis
Financial Services
StaffAI/MLEngineer
Neural analysis suggests this role is
optimal for Staff candidates.
“Staff AI/ML Engineer at RBC Borealis. Skills: ML model serving, ML lifecycle management, Python backend development, ML pipeline engineering, Streaming/event-driven architectures, Containerized ML workloads, CI/CD for ML, Observability for ML systems, Scalable distributed backend systems, Site reliability practices. Own the end-to-end lifecycle of machine learning systems—from experimentation and validation all the way to high-throughput production serving. Technical anchor for model operational”
What You'll Achieve.
Shape the foundation on which Canada's largest financial institution runs its most critical AI workloads; Ensure low-latency, high-throughput inference in production; Support online model serving and event-driven ML workflows; Expose ML capabilities to downstream consumers; Embed quality gates and automated testing throughout CI/CD pipelines; Drive incident response and blameless post-mortems; Operate reliably under high load in hybrid cloud environments; Revolutionize finance through world-class research, solutions, and a resilient data platform; Solve critical challenges in the financial industry; Build intelligent, and scalable, data-driven solutions that will help communities thrive and drive innovation for our customers across the bank
Industry & Context.
What They're Looking For.
Must Have
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 Have
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
What You'll Do.
Own the end-to-end lifecycle of machine learning systems—from experimentation and validation all the way to high-throughput production serving
Technical anchor for model operationalization at scale
Setting the bar for reliability
and engineering excellence across our AI platform
and operating scalable ML model-serving infrastructure using SageMaker
or equivalent platforms
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
Owning containerized deployment of ML workloads on OpenShift Container Platform (OCP4) / Kubernetes
including resource optimization
and rollout strategies
Building and maintaining CI/CD pipelines (GitHub Actions) for model validation
embedding quality gates and automated testing throughout
Instrumenting ML services with comprehensive observability—metrics
and traces—using Datadog
or equivalent driving incident response and blameless post-mortems
How You'll Work.
Team & Collaboration
Works directly with leading researchers in machine learning; Works collaboratively
Communication Scope
Well-documented APIs
Full Job Description
**_Job Description_** **Staff AI/ML Engineer** **What 's the opportunity?** 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. **Your responsibilities include:** * 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 observa
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