Verified Job
IT / Software / Data Analyst
Staff AI Application Engineer- job post Royal Bank of Canada
IT / Software / Data Analyst
#782132
Remote / WFH
Royal Bank of Canada
Job Description
Full job description
Job Description
What is the opportunity?
Join RBC as a hands-on technical lead building production-grade GenAI and Agentic AI applications for Cyber, Risk, Regulatory, Control & Security domains. This is an individual contributor role with no direct reports. You'll build the backend services that power autonomous and semi-autonomous AI agents, and work closely with AI Engineers to turn LangChain/LangGraph prototypes into production applications deployed on OpenShift through our CI/CD pipeline. If you want to write Python backend code, ship to production, and work at the intersection of software engineering and agentic AI in a regulated environment, this role is for you.
What will you do?
Build and deploy backend services for Agentic AI applications using Python Django (preferred) or FastAPI, with Celery for async task processing and long-running agent workflows.
Work with AI Engineers to productionize their agents and proof-of-concepts, turning LangChain/LangGraph prototypes into well-tested backend applications that run reliably in production.
Deploy and operate applications on OpenShift Container Platform (OCP) and Kubernetes, using the Helios CI/CD pipeline to ship frequently.
Build and maintain RESTful APIs that serve AI-powered applications, including authentication (OAuth2/JWT), rate limiting, input validation, and security controls appropriate for a regulated bank.
Integrate AI capabilities into backend services: RAG pipelines, MCP (Model Context Protocol), A2A (Agent-to-Agent Protocol), multi-agent systems, and LLM-powered workflows. Profile and optimize AI application performance, including LLM token cost management, caching strategies, latency reduction, efficient agent orchestration, and production observability using OpenTelemetry, Langfuse, or LangSmith.
Write unit and integration tests for backend services and agent workflows, and participate in code reviews to set the quality bar for the team.
Mentor other engineers on backend and productionization best practices, collaborate with product owners, data scientists, and business stakeholders, and contribute to secure coding and AI safety guardrails for a regulated financial services environment.
What do you need to succeed?
Must-Have:
Job Description
What is the opportunity?
Join RBC as a hands-on technical lead building production-grade GenAI and Agentic AI applications for Cyber, Risk, Regulatory, Control & Security domains. This is an individual contributor role with no direct reports. You'll build the backend services that power autonomous and semi-autonomous AI agents, and work closely with AI Engineers to turn LangChain/LangGraph prototypes into production applications deployed on OpenShift through our CI/CD pipeline. If you want to write Python backend code, ship to production, and work at the intersection of software engineering and agentic AI in a regulated environment, this role is for you.
What will you do?
Build and deploy backend services for Agentic AI applications using Python Django (preferred) or FastAPI, with Celery for async task processing and long-running agent workflows.
Work with AI Engineers to productionize their agents and proof-of-concepts, turning LangChain/LangGraph prototypes into well-tested backend applications that run reliably in production.
Deploy and operate applications on OpenShift Container Platform (OCP) and Kubernetes, using the Helios CI/CD pipeline to ship frequently.
Build and maintain RESTful APIs that serve AI-powered applications, including authentication (OAuth2/JWT), rate limiting, input validation, and security controls appropriate for a regulated bank.
Integrate AI capabilities into backend services: RAG pipelines, MCP (Model Context Protocol), A2A (Agent-to-Agent Protocol), multi-agent systems, and LLM-powered workflows. Profile and optimize AI application performance, including LLM token cost management, caching strategies, latency reduction, efficient agent orchestration, and production observability using OpenTelemetry, Langfuse, or LangSmith.
Write unit and integration tests for backend services and agent workflows, and participate in code reviews to set the quality bar for the team.
Mentor other engineers on backend and productionization best practices, collaborate with product owners, data scientists, and business stakeholders, and contribute to secure coding and AI safety guardrails for a regulated financial services environment.
What do you need to succeed?
Must-Have:
