Production Support Engineering LMTS

Description
Opportunity & Product

Join an agile team with deep startup roots. We operate as a high‑velocity ‘startup‑within‑Salesforce,’ following our recent acquisition. You’ll be managed by the same founders and engineers who built the original company, offering the autonomy of a small team backed by the global scale and trust of Salesforce.

We have successfully moved past the "0 to 1" phase. We have a product that works, customers who love it, and the backing of Salesforce. Now, we are entering the "1 to 100" phase: scaling our architecture to handle global demand, hardening our systems for enterprise‑grade resilience, and integrating deeply with the Agentforce ecosystem. This is your chance to help lead that transition.

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.

• 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/Open Tofu, and AWS/GCP/Azure.
• Coding Mastery:
  Ability to write and review production‑level code in Golang, Type Script, 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, Git…