RB Global (NYSE: RBA) is a leading, omnichannel marketplace that provides value-added insights, services and transaction solutions for buyers and sellers of commercial assets and vehicles worldwide. Operates auction sites in 13 countries Serves customers in more than 170 countries across various asset classes Marketplace brands include Ritchie Bros. and IAA Offers end-to-end asset management and data-driven intelligence through Rouse Services Innovative technology platform for equipment lifecycle management via SmartEquip Leader in live simulcast auction technologies with Xcira Online marketplace for heavy haul transport through Veritread
Manager, Software Engineering
Location
United States
Posted
10 days ago
Salary
$129K - $161K / year
Job Description
The Software Engineering Manager will lead a new Product Engineering team tasked with designing and building the next generation of Agentic AI-powered products for SmartEquip. While the core focus is product delivery, this team is pioneering SmartEquip’s AI-native ecosystem. Therefore, this highly technical, hands-on leader will architect the robust agent platform and backend microservices, while building the foundational end-to-end product platform required to scale, verify, deploy, secure, and monitor AI agents.
- Key Responsibilities:
- Product & Team Leadership: Manage multiple Product Engineering projects and a cross-functional engineering staff, including Python backend engineers, AI engineers, and Test Automation engineers.
- Architectural Ownership: Architect, design, code, review and guide the team in developing scalable, efficient, and performant code for both the Agentic AI products, the backend microservices that power them, and the underlying platform required to scale and secure them.
- AI-Native Development: Champion an AI-first approach by deeply integrating autonomous coding agents into the team's daily workflows and SDLC.
Intelligent Systems: Lead the development of intelligent retrieval systems—from RAG pipelines to agentic architectures that reason across multiple data sources. - Pipeline & Deployment: Work closely with DevOps/SRE to plan, shape, and own the team's end-to-end delivery process, ensuring robust deployment and monitoring for production AI and microservice systems.
- Strategic Tooling: Evaluate and select the right agentic frameworks (e.g., LangGraph, CrewAI, MCP, Google ADK, A2A, Agent Stack) and tools, partnering with SmartEquip technical leadership. We prioritize a deep foundational understanding of AI ecosystems and model behaviors over transient framework hype, ensuring engineers are efficient and proactive.
- Verification & Security: Build automated test and verification pipelines tailored for AI systems, operating with an accuracy-first mindset to manage hallucination risks and ensure secure data access.
- Skills and Experience:
- Engineering Leadership: At least 3 years of experience managing engineers, with a proven ability to independently architect, problem-solve, and develop robust solutions.
- Technical Stack: Polyglot full-stack background with in-depth, hands-on production expertise in Python. Proven experience building scalable backend microservices and RESTful APIs using frameworks like Django or Flask.
- Modern Frameworks & Libraries: Highly knowledgeable in popular Python ecosystems, with specific expertise in data validation and API frameworks like Pydantic and FastAPI, which are critical for agent data structuring.
- AI & Agentic Mastery: Deep experience with context engineering, designing agentic architectures, and building automated agent delivery pipelines. Proven ability to build, secure, and monitor production-ready retrieval systems (RAG). Extensive experience using and directing autonomous coding agents is expected.
- Cloud Infrastructure: Experienced working with cloud platforms (GCP, AWS) and deploying AI workloads at scale, with specific preference for AI cloud platforms like Vertex AI.
- Quality & Verification: Experienced with Test Automation and a strong understanding of how to build automated verification for AI outputs where accuracy has serious consequences.
- Expected Outcomes (First 12 Months):
- The first year will establish the technical and process foundation for SmartEquip's Agentic AI ecosystem.
- Define the Standard: Build the AI SDLC and establish the Agentic AI development standards and guidelines for your team.
- Build the Foundation: Architect and implement the end-to-end delivery platform needed to build, scale, verify, and secure our new AI agents and their corresponding services.
- Deliver the Product: Successfully launch the MVP of SmartEquip's first Agentic AI product.
- Operationalize AI: Establish a robust, AI-native CI/CD pipeline and monitoring system in close partnership with the SRE team.
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