Production Support Engineering - Cloud DevOps Engineer SMTS

Salesforce•San Francisco, CA

About The Position

Salesforce is seeking a Senior Member of Technical Staff (SMTS) Cloud & DevOps Engineer to design, build, automate, and operate reliable, secure, and scalable cloud infrastructure and production platforms. The role works with software engineers, infrastructure teams, security partners, and product teams to improve service availability, deployment reliability, operational efficiency, and developer productivity. It is an engineering-focused DevOps role in which automation and software development are expected to replace repetitive manual operations. The engineer should approach ambiguous infrastructure and production problems methodically, understand application and infrastructure behavior, and use modern engineering practices, including AI-assisted development, to improve reliability and delivery effectiveness. The position spans the full engineering lifecycle, from requirements refinement and design through development, testing, deployment, monitoring, production support, and operational improvement. It includes independently delivering scoped cloud, DevOps, reliability, and automation improvements after alignment on the technical plan and architecture, while seeking guidance on decisions with broader architectural, security, or product impact.

Requirements

6+ years of experience in a senior or lead role in Cloud DevOps engineering.
Experience in performance engineering and benchmarking.
Experience designing, deploying, or operating production workloads on public cloud platforms such as AWS, Azure, or Google Cloud Platform.
Experience developing production software in Core Java, Python, or another object-oriented programming language.
Strong understanding of cloud infrastructure concepts, including compute, storage, networking, load balancing, identity and access management, security, high availability, and disaster recovery.
Experience with containerization and cloud-native technologies such as Docker, Kubernetes, or comparable container orchestration platforms.
Experience with Infrastructure as Code and configuration automation using tools such as Terraform, CloudFormation, Ansible, Helm, or comparable tooling.
Experience building, maintaining, or improving CI/CD pipelines and automated build, test, deployment, and release workflows.
Development or automation experience using Python, Go, Java, or another programming language.
Ability to build maintainable, production-quality automation, tools, services, or APIs.
Familiarity with databases such as Postgres, HBase, Oracle, or similar, and database cluster technologies such as Oracle RAC.
Experience with Linux systems and working knowledge of operating systems, networking, processes, memory, storage, and performance fundamentals.
Experience with production observability using logs, metrics, traces, dashboards, and alerting platforms.
Understanding of distributed-system and reliability concepts, including scalability, fault tolerance, resiliency, service dependencies, capacity, availability, and performance.
Experience troubleshooting complex production issues across application, infrastructure, networking, and cloud layers.
Experience working across the software and infrastructure delivery lifecycle: design, implementation, testing, deployment, monitoring, incident response, and continuous improvement.
Experience using AI-assisted development tools or techniques for automation, software development, troubleshooting, testing, debugging, or documentation.
Strong analytical and problem-solving skills, including the ability to diagnose complex infrastructure and production problems systematically.
Effective written and verbal communication skills and the ability to collaborate across engineering and operational teams.

Nice To Haves

Experience operating large-scale distributed systems or high-availability production platforms.
Experience with Kubernetes ecosystem technologies, service discovery, ingress, service mesh, autoscaling, or container platform engineering.
Experience building internal developer platforms, self-service infrastructure, deployment frameworks, or operational automation.
Experience developing backend services, APIs, command-line tools, controllers, operators, or automation frameworks.
Experience with Site Reliability Engineering (SRE) practices, including service level objectives (SLOs), service level indicators (SLIs), error budgets, capacity management, and incident management.
Experience with cloud security, IAM, secrets management, vulnerability management, policy enforcement, and infrastructure security controls.
Experience with observability technologies such as Prometheus, Grafana, OpenTelemetry, Splunk, or comparable platforms.
Experience with databases, caching systems, messaging platforms, or distributed data infrastructure.
Familiarity with multi-region, multi-cloud, disaster recovery, or large-scale cloud migration architectures.
Familiarity with Agile development practices and iterative release cycles.

Responsibilities

Design, build, automate, and operate secure, reliable, and scalable cloud infrastructure and production platforms.
Independently deliver well-scoped cloud, DevOps, reliability, and automation improvements after alignment on the overall technical plan and architecture.
Develop software, automation frameworks, scripts, APIs, and engineering tools in Python, Go, Java, or comparable languages to reduce manual operations and improve platform reliability.
Build and maintain Infrastructure as Code for repeatable provisioning, configuration, deployment, and lifecycle management of cloud resources.
Develop and improve CI/CD pipelines that automate build, testing, security validation, deployment, rollback, and release processes.
Deploy and operate containerized workloads using Kubernetes or comparable cloud-native platforms.
Apply cloud and distributed-systems concepts, including scalability, fault tolerance, service communication, networking, availability, resiliency, and capacity management.
Build and improve observability using metrics, logs, traces, dashboards, alerts, and health checks to provide actionable visibility into production systems.
Diagnose production incidents using telemetry and system-level analysis, identify contributing factors and root causes, and implement improvements that prevent recurrence.
Identify repetitive operational activities and convert them into safe, testable, and maintainable automation.
Participate in production support and incident response, troubleshooting complex issues across application, cloud, Kubernetes, networking, storage, and infrastructure layers.
Contribute to infrastructure and platform design discussions, identify implementation and operational tradeoffs, and seek guidance when decisions have broader architectural, security, or product impact.
Apply security and reliability practices throughout infrastructure and software delivery, including access controls, secrets management, change validation, testing, rollback, and deployment safeguards.
Participate in the full engineering lifecycle, including requirements refinement, design, development, code review, testing, deployment, monitoring, production support, and operational improvement.
Use AI-assisted development tools responsibly to accelerate automation and engineering work, improve code and configuration quality, assist troubleshooting and testing, and increase team effectiveness while applying appropriate security and quality controls.
Collaborate with software engineers, infrastructure teams, security teams, and other cross-functional partners.
Communicate technical and operational decisions clearly and contribute to engineering documentation and runbooks.