At a Glance
- Tasks: Design and build AI agents for software delivery, automating key processes.
- Company: Join RBC, a leader in innovative banking technology.
- Benefits: Competitive salary, health benefits, remote work options, and growth opportunities.
- Other info: Dynamic team environment with endless learning and career advancement.
- Why this job: Be at the forefront of AI-driven development and make a real impact.
- Qualifications: Experience in software engineering and familiarity with AI tools.
The predicted salary is between 135000 - 165000 $ per year.
What is the Opportunity?
We are building the agentic platform that will let every development team at RBC execute AI driven SDLC - our spec-driven, agent-assisted software delivery methodology - at scale. This is a hands-on platform engineering role inside DevOps: you won't be running one team's pipeline, you'll be building the harness, skills, and agents that every team's development workflow runs on. If you're the kind of engineer who has already wired tools like Claude Code into your own workflow with custom skills and MCP servers to make yourself dramatically more productive, and you want to turn that instinct into a platform used across an entire bank, this is that role.
What will you do?
- Design and build agents across the full software delivery lifecycle: specification creation, technical spec generation, build-and-test agents, and operational agents that automate deployment, release, PIV (post-implementation verification), and post-deployment tasks.
- Build these agents on coding harnesses via custom skills and tools (including MCP servers), on managed agent platforms such as Devin, and as native agents using frameworks like LangChain and LangGraph - choosing the right substrate for each use case.
- Implement and extend spec-driven development workflows (e.g., specify - clarify - plan - tasks - analyze - implement, in the style of GitHub spec-kit / SpecKit) as the backbone of the platform's golden path.
- Build the technical plumbing that enforces AI-DLCs gates (e.g., Spec Ready, PR Ready) as machine-checked controls rather than advisory guidance.
- Continuously push what frontier models and coding harnesses like Claude Code can automate - both at a personal productivity level and as reusable capability that scales across other development teams.
- Partner with DevOps, security, and platform teams to get new agents and skills safely into production and into the shared skill/tool catalog.
- Evaluate emerging agentic AI platforms, frameworks, and design patterns.
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