Production Support Engineering, Salesforce

• 5+ years of experience in SRE, Production Engineering, or Backend Engineering with a heavy focus on operations and scale
• Proven Scaling Experience: You have previously helped take a product through a high-growth phase (the "1 to 100" journey), dealing with the technical debt and architectural shifts that come with it
• Technical Breadth: Strong proficiency in Kubernetes, Terraform/OpenTofu, and AWS/GCP/Azure
• Coding Mastery: Ability to write and review production-level code in Golang, TypeScript, or Python—you view automation as a software engineering problem
• Systems Expert: Deep understanding of distributed systems, including how to debug complex interactions between microservices, databases, and AI agents
• Low-Ego Collaboration: Experience working within a senior team of Principal engineers, capable of both leading specific initiatives and supporting the broader group’s technical vision
• A demonstrated, genuine AI-first approach to engineering. Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty
• Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows
• Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready

Desirable

• As a Production Support Engineer (LMTS), you will be a senior technical lead within our embedded reliability team. You aren’t building the foundation alone—you’ll work alongside a group of engineers and product owners to ensure the Agentforce for Supply Chain platform is the most reliable AI-powered engine in the industry
• This is a role for an engineer who loves the "scaling" problem. You will focus on production excellence, performance tuning, and infrastructure automation. Because you are embedded in the product organization, you’ll have a seat at the table during design reviews, ensuring that as we add new agentic capabilities, they are built to scale from day one
• Scaling & Reliability: Own the reliability roadmap for major product areas, working to transition our systems from startup-speed architectures to highly-available, global-scale enterprise solutions
• Collaborative Leadership: Partner with PMTS-level engineers to refine our infrastructure strategy, contributing senior-level perspectives on system design, capacity planning, and bottleneck identification
• Infrastructure as Code: Maintain and evolve our automated environments, focusing on making our "infrastructure-as-plugins" model more robust and developer-friendly
• AI Operations (AIOps): Support the scaling of our AI/ML infrastructure, ensuring our models have the GPU resources and data pipelines required to deliver real-time supply chain insights
• Production Excellence: Lead the "1 to 100" hardening of our observability stack. You won’t just respond to incidents; you’ll build the tooling that prevents them and the telemetry that explains them
• Performance Engineering: Deep-dive into SQL optimization, API latency, and cross-service communication to ensure our data-intensive supply chain platform remains performant under heavy load
• AI-First Workflow: Lean into the future of engineering by using AI tools (Claude Code, etc.) to automate routine operational tasks and accelerate infrastructure delivery
• Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably
• Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance