AI​/ML Engineer

Staff AI/ML Engineer  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.
Responsibilities   Designing, building, and operating scalable ML model‑serving infrastructure using Sage Maker, 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 Open Shift Container Platform (OCP4) / Kubernetes, including resource optimization, autoscaling, and rollout strategies.
Building and maintaining CI/CD pipelines (Git Hub 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.
Qualifications   Strong, production‑proven experience with ML model serving and lifecycle management using Sage Maker, 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 Open Shift Container Platform (OCP4) / Kubernetes for deploying and operating containerized ML workloads.
Proven experience building and maintaining CI/CD pipelines with Git Hub 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 Mongo

DB 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.
Benefits   Become part of a team that thinks progressively and works collaboratively. We care about seeing each other reach full potential.
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.
Ability to make a difference and lasting impact from a local‑to‑global scale.
RBC 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.

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