Optimize Kubernetes (GKE and EKS) clusters through precision resource requests, limits, and dynamic autoscaling to ensure compute fleets scale efficiently with real-time customer demand.
Proactively plan capacity and right-size stateful backends, queues, BigQuery storage, and vector databases using empirical workload patterns and growth forecasts to keep production ahead of customer usage.
Lead the lifecycle management, staging rollouts, upgrades, and automated rollback strategies for production databases and search infrastructure to eliminate downtime and mitigate operational risk.
Establish comprehensive service-level objectives (SLOs), automated alerting, distributed agent tracing, and Datadog monitoring that reflect true user experience and eliminate operational noise.
Build secure, modern CI/CD pipelines, reproducible staging environments, and developer tooling to accelerate software delivery and empower application engineers building on the AI platform.
What You Bring
5+ years of experience building and operating production infrastructure at scale, with deep hands-on expertise across Google Cloud (GKE, Vertex AI, IAM, VPC networking, BigQuery, Cloud Monitoring).
Proven AWS production experience (EKS, Lambda, IAM, RDS/Aurora, SQS/SNS) and infrastructure management using Terraform across multi-cloud environments.
Production Kubernetes expertise, including autoscaling, resource tuning, cluster lifecycle management, Helm, and GitOps workflows (Argo CD or equivalent).
Practical experience running LLMs in production via provider APIs, including quota management, load balancing, and multi-provider routing (Vertex AI, Bedrock, Anthropic).
Demonstrated experience deploying and operating distributed vector databases, search engines, and stateful data stores on Kubernetes.
Strong background in observability design, including defining meaningful alert policies, SLOs, Datadog dashboards, and incident response runbooks.
Track record of executing measurable performance and cost optimizations across cloud architectures, with the ability to clearly articulate engineering trade-offs.
Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
Strong-fit Background Might Include
Senior MLOps Engineer / AI Platform Engineer
Senior Cloud Infrastructure Engineer (GCP / AWS)
Senior Site Reliability Engineer (Kubernetes & Stateful Distributed Systems)
Platform / Developer Experience Engineer (AI/ML Enablement)
Discover our recruiting process and interview tips from our talent acquisition team on our #LifeAtProcore blog.
Additional Information
Base Pay Range
140,960.00 - 193,820.00 USD Annual
This role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate’s job-related skills, experience, education or training, and location.
For Los Angeles County (unincorporated) Candidates
Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.
