Backend Software Engineer - Python & Pipeline Orchestration; Cloud​/AI Platform

W2 Contract

Pay Rate: $55 - $65 per hour

Location: Cupertino, CA - Remote Role

We are seeking a highly skilled Backend Software Engineer to join our platform engineering team focused on building scalable data and machine learning pipeline orchestration services within our internal cloud ecosystem. This role sits at the intersection of backend engineering, data platforms, and infrastructure, enabling teams to develop, deploy, and manage end-to-end pipelines across a multi-cloud environment.

• Design, build, and maintain backend services and APIs for pipeline orchestration using Python
• Develop and manage end-to-end workflows for data ingestion, data processing and transformation, machine learning training and evaluation, and model deployment and lifecycle management
• Build scalable and reliable orchestration systems that integrate with internal data and AI platforms.
• Collaborate with cross-functional teams to enable seamless pipeline execution across services.
• Ensure system performance, scalability, and reliability in a distributed, multi-cloud environment (AWS, GCP)
• Troubleshoot production issues across application, pipeline, and infrastructure layers
• Contribute to system design and architecture decisions for next-generation AI and data platforms.

• Strong backend engineering experience with Python (required)
• Experience building APIs and services using frameworks such as Flask, FastAPI, or gRPC
• Hands‑on experience working with data pipelines or machine learning pipelines
• Solid understanding of Workflow orchestration concepts, distributed systems, and API design and service architecture
• Familiarity with Kubernetes and containerized environments
• Basic understanding of pods, deployments, and services
• Experience deploying or operating applications in Kubernetes environments
• Experience working in cloud environments (AWS, GCP, or similar)

• Experience with orchestration tools such as Apache Airflow or Flyte
• Exposure to ML lifecycle workflows (training, evaluation, deployment)
• Experience with multi‑cloud or hybrid cloud architectures
• Familiarity with data platforms, feature stores, or model serving systems
• Strong debugging and troubleshooting skills across logs, monitoring, and distributed systems

Bayside Solutions, Inc. is not able to sponsor any candidates at this time. Additionally, candidates for this position must qualify as a W2 candidate.

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