Backend Software Engineer
Bayside Solutions
Cupertino, CA, United States
8 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$124,800.0 - $145,600.0
Working hours
Regular working hours
Job source
Tech stack
Application Programming Interfaces (APIs)
Artificial Intelligence
Airflow
Amazon Web Services
BASIC (Programming Language)
Software Debugging
Distributed Systems
Python (Programming Language)
Machine Learning
Service-Oriented Architecture
Workflow Management Systems
Data Processing
+13 more
Google Cloud
Data Ingestion
Flask (Web Framework)
Multi-Cloud
HybridCloud
Backend
Fastapi
Containerization
Kubernetes
Data Management
Machine Learning Operations
Api Design
Data Pipelines
Job description
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, 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, Google Cloud Platform)
- Troubleshoot production issues across application, pipeline, and infrastructure layers
- Contribute to system design and architecture decisions for next-generation AI and data platforms.
Requirements
- 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, 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
Preferred Qualifications
- 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
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