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Senior AI Engineer, Cloud Architecture

Cloud EngineerCloud EngineerFull TimeRemoteTeam 1,001-5,000Since 2007Company SiteLinkedIn

Location

United States

Posted

8 days ago

Salary

Not specified

Bachelor Degree3 yrs expEnglishAWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformJavaKafkaKubernetesPythonRabbit MQ

Job Description

• Design and implement scalable AI service architectures in cloud environments (Azure preferred; AWS or GCP acceptable) • Build event-driven systems using queues and messaging platforms (e.g., Azure Service Bus, RabbitMQ, SQS) to support asynchronous AI workloads. • Implement event streaming and real-time processing pipelines (e.g., Kafka, Azure Event Hubs, Pub/Sub, Kinesis). • Architect, maintain, and scale containerized AI services deployed to Kubernetes, with emphasis on Azure Kubernetes Service (AKS). • Design orchestration layers that manage model calls, downstream services, retries, rate limits, and failure handling. • Optimize system performance under load, including horizontal scaling, autoscaling policies, resource management, and cost control. • Implement WebSocket or real-time client communication patterns for interactive AI applications. • Contribute to infrastructure-as-code and CI/CD practices for AI service deployment, collaborating with CloudOps, DevOps, and application engineering teams to ensure reliability, availability, and operational standards are met. • Partner with Product and business stakeholders to translate projected traffic, adoption, and growth targets into scalable technical architectures and capacity plans and debug production level issues as needed.

Job Requirements

  • 3-5 years of software engineering experience with strong fundamentals in object-oriented programming, design patterns, and distributed system design.
  • Professional experience in Python, C#, Java, or a similar language used in production systems.
  • Strong hands-on experience with containerization (Docker) and Kubernetes-based orchestration (AKS preferred).
  • Experience integrating AI/LLM workloads into enterprise-grade distributed systems.
  • Experience designing APIs and backend systems that support high concurrency and real-time interactions.
  • Experience designing event-driven architectures using messaging systems (Azure Service Bus, RabbitMQ, SQS).
  • Experience implementing event streaming systems (Kafka, Azure Event Hubs, Pub/Sub, Kinesis).
  • Experience deploying AI systems in cloud environments (AWS, Azure, GCP).
  • Experience in Databricks (model serving endpoints, ML Flow)

Benefits

  • Health insurance
  • Retirement plans
  • Paid time off
  • Flexible work arrangements
  • Professional development

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