Senior Cloud Platform Engineer (SMTS)

Salesforce•Bellevue, WA

6d•Hybrid

About The Position

Salesforce is seeking a Senior Member of Technical Staff (SMTS) for their Monitoring Cloud team. This role is crucial for maintaining the reliability of Salesforce systems. The engineer will be responsible for productizing infrastructure to ensure monitoring capabilities scale with Salesforce's multi-cloud footprint. The position focuses on bridging high-level feature design with deep-system stability, automating processes across AWS and GCP, and securing air-gapped environments. This is an AI-first engineering role, utilizing AI-assisted development tools for various tasks to enhance productivity and focus on architecture, security, and customer outcomes.

Requirements

5+ years Proven track record in Distributed systems, API platforms, Infrastructure Engineering, Observability or DevOps at scale.
Proficiency with Kubernetes (K8s) and Terraform.
Hands-on experience managing infrastructure in AWS and/or GCP.
Proficiency in programming languages (e.g., Java, Python).
Experience managing or extending monitoring tools (e.g., Grafana), messaging systems (Kafka etc), elastic search, caching frameworks.
Understanding of authN/authZ security protocols, particularly in managing isolated or restricted network environments.
AI-assisted development fluency: demonstrated use of AI coding assistants (e.g., Claude Code) as part of a daily engineering workflow, able to prompt effectively, critically evaluate generated code, and integrate AI into IaC, testing, and automation pipelines.

Responsibilities

Design and implement automation frameworks using Terraform and Kubernetes to manage monitoring infrastructure.
Standardize "paved path" deployments across AWS and GCP, eliminating manual configuration errors and ensuring global consistency.
Use AI-assisted tooling as the default for authoring, refactoring, and reviewing IaC modules, Helm charts, and automation scripts.
Own the lifecycle of the Monitoring Cloud stack, including version upgrades and performance tuning.
Productize core components (e.g., Grafana, custom Terraform providers) to make them consumable as reliable services by internal engineering teams.
Leverage AI for upgrade planning, release-note analysis, migration scaffolding, and boilerplate-heavy productization work.
Deploy and manage the full monitoring stack within highly isolated, air-gapped environments.
Ensure that secure customer segments receive the same level of observability and reliability as public cloud offerings.
Apply AI assistance during development of artifacts for air-gapped environments and operate them with disciplined, human-driven workflows.
Participate in the team’s on-call rotation, providing technical expertise to maintain strict SLAs and availability targets.
Conduct root-cause analysis (RCA) for complex system failures and implement long-term preventative fixes.
Address support requests with a “customer first” mindset.
Use AI as a co-pilot during incident response and RCA.
Design and deliver platform features that adhere to enterprise standards while pioneering AI-driven development practices.
Contribute to and evolve the team's AI-assisted development playbook.