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# Senior Software Engineer, AI Platform

- **Company:** Procore
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Salary:** $140,960 - $193,820
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, BigQuery, Cloud Computing, Databases, Data Stores, Programming Tools, Identity and Access Management, Project Management Software, Octopus Deploy, Software Engineering, AI Infrastructure, Google Cloud, Load Balancing, Cloud Monitoring, Autoscaling, Delivery Pipeline, Large Language Models, Multi-Cloud, Amazon Virtual Private Cloud (VPC), Agentic-AI, AI Platforms, Kubernetes, Information Technology, Amazon Bedrock, Search Engines, Virtual Agents, Amazon Simple Queue Service (SQS), Terraform
- **Published:** November 14, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-ai-platform-austin-tx--84c16318-c955-4d83-bba7-6a45d2cc6048

## About the Role

Software Engineering, Artificial Intelligence (AI), Construction, Artificial Intelligence (AI), Risk Management, Technical Leadership, Cloud Computing, Data Science, Software Engineering, Artificial Intelligence (AI) Agents, * 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.

## Description

We are looking for a Senior AI Infrastructure Engineer to ensure the platform infrastructure powering our AI agents remains high-performing, resilient, and cost-efficient as customer adoption scales. As the Senior AI Infrastructure Engineer on our AI Infrastructure team, you will work closely with application engineers, data scientists, and platform teams to build multi-cloud deployment pipelines, scale vector databases, and optimize our multi-provider LLM gateway. Your strengths in multi-cloud infrastructure (GCP/AWS), Kubernetes orchestration, and AI/LLM production operations will drive your success.

You will report to the VP of Engineering and will be a 2 days per week hybrid role based in our Austin, TX office.

What You'll Do

 * Operate, scale, and maintain production workloads across Google Cloud and AWS to provide a resilient, unified multi-cloud foundation for Procore's AI agent portfolio.
 * Manage production AI model workloads across Vertex AI, Amazon Bedrock, and Anthropic, implementing explicit provider fallback, regional availability routing, and GPU-backed self-hosted model serving on Kubernetes.
 * 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.