Cloud / DevOps Engineer (Infra & IaC)
About The Position
This role is for one of our clients. 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. You will apply your real-world infrastructure engineering experience to evaluate technical workflows, create high-quality reference solutions, identify gaps in AI-generated outputs, and help establish rigorous standards for cloud and DevOps reasoning. This is a full-time engagement requiring 40 hours per week, Monday through Friday. 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.
Requirements
- 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.
Nice To Haves
- 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.
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.
Benefits
- Equal employment opportunities to all qualified candidates.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
