Software Engineer (Cloud / AI Platform) W2 Contract Pay Rate: $50 - $60 per hour Location: Cupertino, CA - Remote Role Job Summary: We are looking for a talented Backend Software Engineer to join our AI Platform Engineering team. In this role, you will help build the core platform that powers data pipelines, machine learning workflows, and AI services across our multi-cloud environment. Duties and Responsibilities: Design, build, and maintain scalable backend services and APIs using Python. Develop workflow orchestration services that support data ingestion, transformation, machine learning training, evaluation, deployment, and monitoring. Build reusable platform services that enable engineering teams to create and manage end-to-end AI and data pipelines. Design and implement REST and gRPC APIs for internal platform services. Deploy and operate applications in Kubernetes environments. Design reliable, scalable, and highly available distributed systems. Collaborate with platform, infrastructure, data engineering, and machine learning teams to deliver platform capabilities. Troubleshoot production issues across applications, orchestration pipelines, and Kubernetes infrastructure. Contribute to system architecture, technical design, and engineering best practices. Improve platform performance, scalability, reliability, and operational excellence. Requirements and Qualifications: Strong software engineering fundamentals with experience building production backend systems. Strong Python development experience. Experience designing scalable, maintainable, and testable software. Experience building APIs using FastAPI, Flask, gRPC, or similar frameworks. Experience with asynchronous programming and modern backend development practices. Experience building or supporting workflow orchestration systems. Solid understanding of workflow scheduling, task dependencies, retries, and pipeline monitoring. Experience with Apache Airflow or similar orchestration platforms. Experience working with data or machine learning pipelines. Understanding of the machine learning lifecycle, including data ingestion, transformation, feature engineering, model training, evaluation, deployment, and monitoring. Experience enabling machine learning workflows rather than building machine learning models. Experience deploying and operating applications in Kubernetes environments. Good understanding of pods, deployments, services, ConfigMaps, Secrets, ingress, rolling deployments, and autoscaling. Experience with any managed Kubernetes platform such as Amazon EKS, Google Kubernetes Engine, Azure Kubernetes Service, Alibaba ACK, or similar. Understanding of distributed system design, service architecture, scalability, resiliency, asynchronous processing, and fault tolerance. Experience developing applications in AWS, Google Cloud Platform, Azure, or similar cloud environments. Preferred Qualifications: Apache Airflow Flyte or Argo Workflows Docker and containerized applications Microservices architecture CI/CD pipelines Infrastructure as Code Observability, monitoring, and logging platforms Experience supporting AI or machine learning platform engineering Experience with feature stores, model registries, or model serving platforms Bayside Solutions, Inc. is not able to sponsor any candidates at this time. Additionally, candidates for this position must qualify as a W2 candidate. Bayside Solutions, Inc. may collect your personal information during the position application process. Please reference Bayside Solutions, Inc.'s CCPA Privacy Policy at ;/span>