Senior Software Engineer, AI Platform
About The Position
Procore Technologies is seeking a Senior AI Infrastructure Engineer to ensure the platform infrastructure powering its AI agents remains high-performing, resilient, and cost-efficient as customer adoption scales. The engineer will work closely with application engineers, data scientists, and platform teams to build multi-cloud deployment pipelines, scale vector databases, and optimize the multi-provider LLM gateway. The role is based in the Austin, Texas office and is hybrid, with two days per week in the office. It reports to the VP of Engineering. The position focuses on operating and improving production AI infrastructure across Google Cloud and AWS, including Kubernetes environments, AI model workloads, databases, search systems, monitoring, and CI/CD. The engineer will help ensure platform reliability, capacity, security, performance, and cost efficiency as usage grows.
Requirements
- At least 5 years of experience building and operating production infrastructure at scale.
- Hands-on expertise with Google Cloud, including GKE, Vertex AI, IAM, VPC networking, BigQuery, and Cloud Monitoring.
- Production AWS experience, including EKS, Lambda, IAM, RDS/Aurora, and SQS/SNS.
- Experience managing infrastructure with Terraform across multi-cloud environments.
- Production Kubernetes expertise, including autoscaling, resource tuning, cluster lifecycle management, Helm, and GitOps workflows such as Argo CD or equivalent.
- Practical experience running LLMs in production through provider APIs, including quota management, load balancing, and multi-provider routing.
- Experience deploying and operating distributed vector databases, search engines, and stateful data stores on Kubernetes.
- Strong background in observability design, including alert policies, SLOs, Datadog dashboards, and incident response runbooks.
- Track record of measurable performance and cost optimizations across cloud architectures, and ability to articulate engineering trade-offs.
- Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.
Nice To Haves
- Background as a Senior MLOps Engineer or AI Platform Engineer.
- Background as a Senior Cloud Infrastructure Engineer (GCP/AWS).
- Background as a Senior Site Reliability Engineer focused on Kubernetes and stateful distributed systems.
- Background as a Platform or Developer Experience Engineer focused on AI/ML enablement.
Responsibilities
- Operate, scale, and maintain production workloads across Google Cloud and AWS to provide a resilient, unified multi-cloud foundation for the AI agent portfolio.
- Manage production AI model workloads across Vertex AI, Amazon Bedrock, and Anthropic, including provider fallback, regional availability routing, and GPU-backed self-hosted model serving on Kubernetes.
- Optimize GKE and EKS clusters through resource requests, limits, and dynamic autoscaling.
- Plan capacity and right-size stateful backends, queues, BigQuery storage, and vector databases based on workload patterns and growth forecasts.
- Lead lifecycle management, staged rollouts, upgrades, and automated rollback strategies for production databases and search infrastructure.
- Establish service-level objectives, automated alerting, distributed agent tracing, and Datadog monitoring that reflect user experience and reduce operational noise.
- Build secure CI/CD pipelines, reproducible staging environments, and developer tooling for application engineers building on the AI platform.
Benefits
- May be eligible for equity compensation and/or bonus incentive compensation.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
