RBC
Financial Services
SeniorAI/MLEngineer-SiteReliabilityEngineering
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“Senior AI/ML Engineer - Site Reliability Engineering at RBC. Skills: Agentic AI platform development, Software reliability, Resiliency, Intelligent automation systems, Machine Learning. Design and implement end-to-end Agentic AI solutions. Develop intelligent automation frameworks”
What You'll Achieve.
Autonomously prevent incidents; Accelerate response times; Transform how we maintain resilience across enterprise systems; Reduce toil; Measurably reduce toil; Reducing MTTR (Mean Time to Resolve); MTTD (Mean Time to Detect); MTTI (Mean Time to Identify); Improving system reliability
Industry & Context.
Autonomously detect anomalies; Identify root causes; Resolve incidents with minimal human intervention; Continuously improve response strategies; Reduce toil
What They're Looking For.
Must Have
ML engineering background with hands-on experience designing, training, and deploying machine learning models in production environments, Proven expertise in Agentic AI frameworks and tools (LangChain, LangGraph, AutoGen, CrewAI, or similar) and building autonomous, multi-agent systems, Deep understanding of Model Context Protocol (MCP) for enabling AI agents to interact with external systems and data sources, Experience building AI agents with tool-calling capabilities, memory management, and reasoning chains, Proficiency in Python and experience with ML libraries (scikit-learn, TensorFlow, PyTorch, or similar), Working knowledge of containerization (Docker), orchestration (Kubernetes/OpenShift), and infrastructure-as-code principles (Ansible, Terraform), Demonstrated ability to translate complex technical concepts into business value and collaborate effectively with cross-functional teams
Nice to Have
Prior experience in Site Reliability Engineering, DevOps, or infrastructure monitoring roles, Familiarity with observability tools (Prometheus, Grafana, ELK stack) and incident management platforms (PagerDuty, ServiceNow), Experience with LLMs, prompt engineering, and retrieval-augmented generation (RAG) architectures, Background in financial services or other highly regulated industries with strict reliability requirements
What You'll Do.
Design and implement end-to-end Agentic AI solutions
Develop intelligent automation frameworks
Build ML-powered monitoring and alerting systems
production-grade solutions on OpenShift and Kubernetes
Implement infrastructure-as-code using Ansible and containerization (Docker)
Partner with incident management and operations teams to translate operational pain points into AI-driven automation opportunities
Establish and track KPIs focused on reducing MTTR
and MTTI while improving system reliability
Lead technical design discussions and contribute to architectural decisions
How You'll Work.
Team & Collaboration
Collaborate effectively with cross-functional teams; Partner with incident management and operations teams
Full Job Description
**_Job Description_** **WHAT IS THE OPPORTUNITY?** Join RBC's Site Reliability Engineering team as a founding member building the bank's **first-ever Agentic AI platform for Software reliability** and**resiliency**. You'll pioneer intelligent automation systems that autonomously prevent incidents, accelerate response times, and transform how we maintain resilience across enterprise systems. This is a rare opportunity to shape the future of AI-driven reliability at scale. Your innovations will protect millions of daily customer transactions and sign-ins. With a clear technical leadership trajectory, you'll architect cutting-edge solutions at the intersection of AI and infrastructure, setting the standard for autonomous operations in financial services. **WHAT WILL YOU DO?** * **Design and implement end-to-end Agentic AI solutions** that autonomously detect anomalies, identify root causes, and resolve incidents with minimal human intervention * **Develop intelligent automation frameworks** using **LangChain** and **LangGraph** to create context-aware agents that learn from incident patterns and continuously improve response strategies * Build ML-powered monitoring and alerting systems that distinguish signal from noise, dramatically reducing false positives and improving MTTD (Mean Time to Detect) and MTTI (Mean Time to Identify) * Architect scalable, production-grade solutions on OpenShift and Kubernetes that process real-time system metrics and telemetry data at enterprise scale * Implement infrastructure-as-code using Ansible and containerization (Docker) to ensure reproducibility, consistency, and rapid deployment across environments * Partner with incident management and operations teams to translate operational pain points into AI-driven automation opportunities that measurably reduce toil * Establish and track KPIs focused on reducing MTTR (Mean Time to Resolve), MTTD, and MTTI while improving system reliability * Lead technical design discussions and contribut
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