Design and evaluate cloud infrastructure, Kubernetes, IaC, AWS, and CI/CD solutions for GenAI training and inference environments. Create technical scenarios, reference solutions, evaluation rubrics, and feedback for AI-generated infrastructure outputs. Troubleshoot production Kubernetes and distributed-system failures while applying cloud architecture, security, reliability, observability, and operational best practices.
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. 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.
RequirementsKey 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.
- 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.
- 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.
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 OpportunityWe 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.
Similar Jobs
Blockchain • Fintech • Mobile • Payments • Software • Financial Services
Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.
Top Skills:
AnomaloApache AirflowApache IcebergAWSBuildkiteDatabricksDatahubDbtGitopsKotlinLookerOmniPrefectPythonSnowflakeTerraform
Big Data • Cloud • Software • Generative AI • Big Data Analytics
Own a US enterprise sales territory focused on landing net-new logos among companies with 1,000–10,000 employees. Build outbound pipeline, run full-cycle deals, lead MEDDPICC-based discovery, collaborate with Solutions Architects on technical evaluations, and engage data and AI executives. The role also contributes customer insights to Product and Marketing, develops segment strategy and sales collateral, and helps build the growing enterprise sales team.
Top Skills:
AICollibraData ObservabilityDatabricksSnowflake
Artificial Intelligence • Fintech • Software • Travel
Lead implementation strategy and customer rollouts for major B2B opportunities, partnering with Sales, Account Management, Product, and Engineering. Manage and coach Implementation Managers, oversee deployment milestones, deliver customer training, drive product adoption, remove implementation blockers, and establish scalable onboarding processes. Serve as the senior implementation voice for strategic customers and ensure smooth transitions to Account Management.
Top Skills:
AIB2B Saas
What you need to know about the Chicago Tech Scene
With vibrant neighborhoods, great food and more affordable housing than either coast, Chicago might be the most liveable major tech hub. It is the birthplace of modern commodities and futures trading, a national hub for logistics and commerce, and home to the American Medical Association and the American Bar Association. This diverse blend of industry influences has helped Chicago emerge as a major player in verticals like fintech, biotechnology, legal tech, e-commerce and logistics technology. It’s also a major hiring center for tech companies on both coasts.
Key Facts About Chicago Tech
- Number of Tech Workers: 245,800; 5.2% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: McDonald’s, John Deere, Boeing, Morningstar
- Key Industries: Artificial intelligence, biotechnology, fintech, software, logistics technology
- Funding Landscape: $2.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Pritzker Group Venture Capital, Arch Venture Partners, MATH Venture Partners, Jump Capital, Hyde Park Venture Partners
- Research Centers and Universities: Northwestern University, University of Chicago, University of Illinois Urbana-Champaign, Illinois Institute of Technology, Argonne National Laboratory, Fermi National Accelerator Laboratory
