Akvelon Inc. (USA) works in the field of software engineering based on a variety of technologies. Our company is a member of the vendor program, which gives employees the opportunity to work with clients in the United States and other countries (Microsoft, Facebook, Airbnb, Dropbox, Pinterest, and many others). This is a great opportunity to learn from and work with leading engineers of world-renowned companies.

About project

Our team is driving innovation in AI cloud infrastructure by benchmarking performance across leading platforms like AWS EKS, Azure AKS, and next-generation clouds such as Cloud Wave. We focus on evaluating and optimizing inferencing efficiency, latency, throughput, and horizontal scalability to push the limits of AI workloads in the cloud. You’ll contribute to enhancing and extending our internal benchmarking framework, helping shape the standards for next-gen AI infrastructure performance.

• Design and implement a benchmarking project aimed at evaluating inference performance, latency, and horizontal scaling across major cloud providers (Google Cloud, AWS, Azure) as well as emerging “neo-clouds” (e.g., CoreWeave).

  
  

• Develop methodologies for consistent, repeatable benchmarking of ML/AI workloads; leverage existing approaches used for IKS/EKS as initial baseline, then extend to create custom benchmarking frameworks.

  
  

• Build scalable, sandboxed test environments enabling automated performance comparison of various compute configurations and cloud platforms.

  
  

• Continuously improve inference pipelines and system efficiency through data-driven analysis of bottlenecks, scaling patterns, and platform differences.

  
  

• Apply best industry practices for observability, performance optimization, and distributed systems benchmarking.

  
  

• Collaborate closely with a small team (1–2 Infra engineers) to design, run, and maintain benchmarking infrastructure and tooling.

  
  

• 5+ years of experience in MLOps, DevOps, or Cloud Engineering roles.
• Strong hands-on expertise with Kubernetes and managed services (GKE, EKS, AKS).
• Proven experience in cloud benchmarking, scalability, and performance optimization.
• Solid understanding of container orchestration, CI/CD, and infrastructure automation (Terraform, Helm, ArgoCD).
• Experience supporting AI/ML workloads, including inferencing and model deployment pipelines.
• Proficiency in Python or Go for automation and performance testing.
• Familiar with monitoring/observability tools (Prometheus, Grafana, Cloud Logging).
• Strong grasp of networking, load balancing, and cloud security fundamentals.

• Career Development

  
  

• Professional Certification

  
  

• Mentorship

  
  

• Medical Insurance

  
  

• Relocation Support

  
  

• Corporate Events

  
  

• Flexible Work Options (hybrid/remote)

  
  

• Paid Time Off and Sick Leave