Senior Lead Engineer, Platform Engineering and Cloud Resilience

Sapience AI Corporation•Seattle, WA

7d•$160,000 - $180,000

About The Position

Sapience AI is hiring an onshore operations lead to own day-to-day platform operations and lead its existing distributed platform engineers. Sapience AI provisions isolated customer environments on Google Cloud using Terraform and operates them to an enterprise zero-trust security standard. The platform supports customers ranging from small communities to very large organizations, including environments that must scale across multiple regions. The role owns how changes reach production and how the cloud estate is operated, while partnering with the Cloud Architect, who owns cloud architecture, and the Security Architect, who owns the security posture. The role reports to the Cloud Architect and supports the development of the next generation of the platform. The position owns the merge-to-production path, including pull request review, plan-and-apply discipline, deployments to customer environments, rollback, and automated quality and security gates. It owns day-two Google Cloud operations, including project and environment lifecycle, identity and access hygiene, quotas, cost visibility, key and certificate rotation, patching, version pins, drift, dashboards, and audit trails. It also operates and improves the Terraform provisioning standard, including modules, state, CI plan-gating, and drift detection. The role designs and participates in the on-call rotation, establishes operational readiness and SLO practices, maintains exercised runbooks, and leads incident response and postmortems. It partners on architecture decisions such as customer-tier sizing and placement, regional expansion, data-tier operations, and cost, without owning architecture or data design. It assists in building the support function—including intake and triage, response targets, initial support hiring, runbook creation, and knowledge-base structure—but does not own the help-desk queue once a support lead is in place. AI-assisted planning, triage, and review are part of platform operations. The role is expected to improve these tools while retaining responsibility for interpretation and decisions. The team’s operating motto is “automate a secure world.” Compensation is a base salary of $160,000–$180,000 plus early equity, with generous health and wellness benefits.

Requirements

At least 10 years operating production systems on a major public cloud, or direct operational ownership of a platform serving more than one million members or customers.
Professional background in technical support, help desk, NOC, or operations, followed by running such a function; experience setting intake, targets, and on-call and being accountable for its performance.
Deep Google Cloud production experience, including GKE, Cloud IAM and workload identity, VPC networking, Cloud KMS, and Cloud SQL or AlloyDB operations.
Daily CI/CD experience and ownership of a merge-to-production path, its gates, and rollback using GitHub Actions or a comparable tool.
Production Terraform experience, including module conventions, remote state, CI plan-gating, and drift; ability to review and improve Terraform authored by others.
Ability to read code, substantively review pull requests, write Python, and understand software design, testing, and delivery.
Experience building or leading an on-call program and holding a pager in a rotation.
Experience leading or developing engineers, such as a support or operations team or junior engineers brought to independence.
Clear written communication for specialist and non-specialist audiences, including task packets, runbooks, postmortems, and status updates.
Authorization to work in the United States without sponsorship; ability to work U.S. Pacific business hours and cover an on-call rotation.

Nice To Haves

Experience operating customer-isolated, single-tenant-per-customer environments at scale.
Google Cloud experience with private networking, service perimeters, and software supply-chain controls.
Experience with multi-region database operations on Cloud SQL or AlloyDB.
ITIL or comparable service-management practice applied to an engineering audience.
Practical understanding of AI agents and agentic workflows in operations, including their failure modes.
Experience leading distributed or follow-the-sun teams across time zones.
A degree in computer science or a related technical field; it is a plus, not a requirement.

Responsibilities

Lead the existing distributed platform engineers, support their professional development, and grow them into engineers able to participate in and hold the on-call rotation.
Own the merge-to-production path for the platform and shared services, including pull request review, plan-and-apply discipline, customer-environment deployment dispatches, rollback, and merge authority for platform repositories.
Own automated quality and security gates and keep them efficient and consistently enforced.
Operate the Google Cloud estate day to day, including project and environment lifecycle, IAM and workload identity hygiene, quotas and reservations, cost visibility, certificate and key rotation, patching, version pins, and drift.
Build NOC-grade dashboards that show customer-environment health, the production path, and cloud-estate status; maintain audit trails for access and exposure.
Operate, review, and extend Terraform provisioning, including module conventions, state layout, version pins, CI plan-gating, and drift detection; enforce standards in CI and review other teams’ work against them.
Design and run the on-call rotation and participate in it; own SLOs, exercised runbooks, incident response, postmortems, and operational-readiness gates for self-operated stateful infrastructure.
Partner with the Cloud Architect on customer-tier sizing and placement, regional expansion, data-tier operations, and cost, representing operational considerations without owning architecture.
Assist with the support intake and triage model, response targets, interviewing for initial support hires, runbook creation, and knowledge-base structure; the support lead owns the day-to-day queue once in place.
Improve inherited AI-assisted planning, triage, and review tools, while owning the interpretation and operational decisions.
Work with engineering teams shipping on the platform, the Security Architect on identity, keys, perimeters, and audit trails, and DBAs on operation of the data tier.
Write task packets, runbooks, status updates, and postmortems; turn repeat requests into automation or self-service and make operational risk legible.