Lead Site Reliability Engineer/Cloud Platform Engineer – SF (Remote) at Skyflow

Skyflow secures the flow of data across datastores, models, and agents. Enterprises turn to Skyflow as their runtime AI data control layer to protect sensitive data, enable safe AI deployment, and unlock full value from their applications, data platforms, and AI systems.

Skyflow is trusted by Fortune 500 enterprises, and leading SaaS companies in financial services, healthcare, retail, travel and hospitality.

Skyflow is headquartered in Palo Alto, California and was founded in 2019. For more information, visit www.skyflow.com http://www.skyflow.com or follow on X https://twitter.com/SkyflowAPI and LinkedIn https://www.linkedin.com/company/skyflow/.

About the role

We're looking for a Platform Engineer to help build and run the infrastructure that powers a multi-tenant, security-sensitive B2B cloud product used by enterprise customers around the world. Our platform spans multi-tenant environments and single-tenant (BYOC) deployments into customer cloud accounts, so the bar for automation, consistency, and reliability is high every environment needs to be provisioned, upgraded, monitored, and recovered the same way, at scale, with minimal manual intervention.

This is a hands-on engineering role, not a ticket-queue ops role. You'll spend most of your time writing Go and Python to turn repeatable infrastructure work into platform capabilities provisioning pipelines, internal CLIs, self-service tooling, and automation that lets the rest of engineering ship without waiting on infrastructure as a bottleneck. You'll also carry deep operational ownership: on-call, incident response, and root-cause analysis on the systems you build.

Collaborate with enterprise customers to understand their infrastructure and security needs, then build reusable, scalable solutions using Terraform, Helm, and self-service tools that can be standardized across customer environments with minimal customization.

You have:

– 8+ years of experience in platform engineering, infrastructure engineering, DevOps, or SRE roles, with real ownership of production cloud infrastructure.

– Strong software engineering skills in Go and/or Python you write tested, maintainable code and think of infrastructure automation as software, not scripting.

– Deep hands-on experience with Kubernetes in production (workload scheduling, networking, autoscaling, upgrades) and Infrastructure-as-Code tooling such as Terraform, Pulumi, or OpenTofu.

– Solid experience with at least one major public cloud (AWS or GCP or Azure); experience operating in all the three is a strong plus given our multi-cloud footprint.

– Track record of building tools or platforms that other engineers use internal CLIs, provisioning frameworks, self-service portals, or automation pipelines not just maintaining existing infrastructure.

– Comfort operating in a B2B environment with enterprise customers, where infrastructure changes carry compliance, security, and contractual weight (e.g. dedicated/BYOC deployments, uptime SLAs, audit requirements).

– Strong incident-response instincts: you can debug distributed systems under pressure, drive a root-cause analysis to a real fix, and communicate clearly during and after an incident.

– A bias toward root-causing and automating away recurring problems over repeatedly firefighting the same issue.

– Lead, mentor, and technically guide a team of junior/early-career platform engineers.

You will:

– Design and build automation that provisions and manages cloud infrastructure end-to-end new environment onboarding, upgrades, scaling, and decommissioning across multiple cloud providers (AWS, GCP) and multiple deployment models (multi-tenant and dedicated/BYOC).

– Write production-grade Go and Python services and CLIs that turn infrastructure operations into self-service platform capabilities for other engineering teams, rather than one-off scripts or manual runbooks.

– Own and evolve the Infrastructure-as-Code stack (Terraform/OpenTofu, Helm, GitOps/ArgoCD) that defines every environment, and drive migrations across the fleet (Kubernetes version upgrades, node pool migrations, service mesh changes) with minimal customer impact.

– Operate and scale core platform services Kubernetes clusters, service mesh (Istio), data stores (Aerospike, PostgreSQL), messaging (Kafka), and GPU-backed inference workloads with a focus on capacity planning, cost efficiency, and right-sizing.

– Build and improve observability and alerting (metrics, logs, synthetic monitoring) so that failures are caught before customers notice, and drive the automation that turns repeat incidents into permanent fixes.

– Work directly with large enterprise customers and their infrastructure, security and platform teams to gather platform requirements. Design solutions that are built once for one customer and then reused across all environments as standard Terraform modules, Helm charts or self-service tooling, with minimal custom work.

– Own the infrastructure side of customer onboarding, from technical discovery to go-live, and act as the bridge between customers and engineering. Anticipate integration gaps early, turn customer requests into well-scoped platform features, and drive feature expansion based on customer demand while keeping the architecture consistent.

– Participate in an on-call rotation, lead incident response for the systems you own, and write root-cause analyses that result in concrete corrective action not just documentation.

– Partner with security and compliance stakeholders to build guardrails (secrets management, access control, network policy, audit logging) directly into the platform, so secure-by-default is the path of least resistance for every team.

– Continuously identify manual, repetitive, or error-prone infrastructure work and eliminate it — the measure of success in this role is less manual toil across the org, not more tickets closed.

Nice to have:

– Experience with service mesh (Istio/Envoy), GitOps workflows (ArgoCD/Flux), or policy-as-code (OPA).

– Experience operating stateful systems in production — distributed databases (Aerospike, PostgreSQL, Cassandra), message queues (Kafka), or GPU-backed inference workloads.

Read the full description on the employer’s site →