Cloud / DevOps Engineer (Infra & IaC)

Weekday AI (YC W21); United States; On-site; USD 103,000 - 152,000; Full time

Job summary

Weekday AI seeks an experienced Cloud/DevOps Engineer (Infra & IaC) to contribute to a cutting-edge GenAI environment, focusing on building and improving large-scale AI training and inference infrastructure.

The role requires hands-on Kubernetes in production, AWS services, and IaC expertise with Terraform or AWS CDK, plus strong CI/CD experience. This full-time engagement expects 40 hours per week, Monday through Friday.

Qualifications

• 4+ years of professional experience in Cloud Infrastructure or DevOps.
• Hands-on Kubernetes in production environments.
• IaC tooling experience with Terraform and/or AWS CDK.
• Strong knowledge of AWS services: Lambda, API Gateway, DynamoDB.
• Experience designing CI/CD pipelines for production workloads.
• Ability to commit to 40 hours per week on weekdays.
• Excellent written and verbal communication skills.
• Strong troubleshooting of distributed systems.

Responsibilities

• Collaborate with research and engineering teams to define infrastructure tasks for AI models.
• Design scenarios covering Kubernetes troubleshooting, AWS integration, and IaC automation.
• Develop reference solutions for complex infrastructure engineering problems.
• Review AI-generated infrastructure solutions for production quality and security.
• Provide structured feedback highlighting gaps and improvements.
• Create evaluation criteria, rubrics, and benchmarks for Kubernetes and CI/CD scenarios.
• Develop production-grade scenarios involving cluster reliability and deployment workflows.

Skills

Kubernetes in production

AWS

Terraform

CI/CD

AWS CDK

GitOps

Distributed systems

Tools

AWS CDK

Terraform

GitOps tooling

Job description

Compensation: $75 - $110 per hour We are seeking an experienced

This role is for one of our clients Compensation: $75 - $110 per hour We are seeking an experienced Cloud / DevOps Engineer (Infra & IaC) to contribute to a cutting-edge GenAI environment focused on building and improving large-scale AI training and inference infrastructure. The ideal candidate will bring strong, hands-on expertise in Kubernetes, AWS cloud services, Infrastructure-as-Code (IaC), and CI/CD. This is a full-time engagement requiring 40 hours per week, Monday through Friday.

Requirements

Key Responsibilities

• Collaborate with research and engineering teams to identify knowledge gaps and improve AI model performance across cloud infrastructure, DevOps, Kubernetes, and Infrastructure-as-Code domains
• Design realistic and technically challenging tasks covering Kubernetes troubleshooting, AWS service integration, infrastructure automation, and production operations
• Develop accurate, detailed reference solutions for complex infrastructure engineering scenarios
• Review and evaluate AI-generated technical solutions for correctness, reliability, scalability, security, and adherence to production best practices
• Provide clear, structured written feedback highlighting technical gaps, incorrect assumptions, and opportunities for improvement
• Create detailed evaluation criteria, rubrics, and benchmarks for assessing Kubernetes troubleshooting, IaC architecture, AWS integrations, and CI/CD reasoning
• Develop scenarios involving cluster failures, infrastructure automation, deployment workflows, service integrations, and operational reliability
• Work closely with other technical subject matter experts to maintain consistency, accuracy, and quality across evaluation datasets
• Translate practical production experience into structured guidance that can be used to improve AI-generated infrastructure solutions

Core Qualifications

• 4+ years of professional experience in Cloud Infrastructure, DevOps, Site Reliability Engineering, Platform Engineering, or a closely related field
• Strong hands-on experience managing Kubernetes in production environments, including diagnosing, troubleshooting, and resolving cluster failures and operational issues
• Experience with Kubernetes beyond simply writing manifests or consuming managed Kubernetes control planes
• Proven production experience with Infrastructure-as-Code, particularly Terraform and/or AWS CDK
• Strong practical knowledge of AWS cloud services, including production integration with services such as:
  • AWS Lambda
  • API Gateway
  • DynamoDB
• Experience designing, implementing, and maintaining CI/CD pipelines for production workloads
• Strong understanding of cloud architecture, infrastructure automation, deployment strategies, observability, reliability, and operational best practices
• Demonstrated career progression with increasing ownership and responsibility in infrastructure, DevOps, or platform engineering
• Ability to commit reliably to 40 hours per week during standard weekdays
• Excellent written and verbal communication skills, with the ability to explain complex technical concepts and engineering decisions clearly
• Strong analytical and troubleshooting abilities, particularly when diagnosing distributed systems and infrastructure failures

Preferred Skills

• Experience working with large-scale cloud infrastructure or highly distributed systems
• Familiarity with Kubernetes networking, security, storage, scaling, and cluster lifecycle management
• Experience implementing infrastructure security and reliability best practices
• Knowledge of AWS architecture patterns and cloud-native application design
• Experience with GitOps, containerization, monitoring, logging, and observability platforms
• Familiarity with modern DevOps and platform engineering methodologies
• Experience reviewing or evaluating technical documentation, engineering solutions, or AI-generated outputs

What You'll Contribute

In this role, your production infrastructure expertise will help establish high-quality standards for AI systems working with complex Cloud, DevOps, Kubernetes, AWS, and IaC problems. You will play a key role in transforming practical engineering knowledge into structured tasks, reference solutions, evaluation frameworks, and high-quality technical feedback that can improve the capabilities of next-generation AI models.

Equal Opportunity

We are committed to providing equal employment opportunities to all qualified candidates. Employment decisions are made without regard to legally protected characteristics, and reasonable accommodations are available throughout the hiring and engagement process upon request.