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# AI Infrastructure Platform Engineer (Python)

- **Company:** Bayside Solutions
- **Location:** Austin, TX, United States (Remote available)
- **Salary:** $114,400.0 - $135,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Big Data, Data Structures, Distributed Systems, Fault Tolerance, Python (Programming Language), Redis, Data Logging, Multithreading, Large Language Models, Grafana, Concurrency, Caching, Backend, Event Driven Architecture, Build Management, Containerization, Kubernetes, Apache Kafka, Machine Learning Operations, Asynchronous Programming, Api Design, Microservices
- **Published:** August 26, 2026
- **Apply:** https://www.dice.com/job-detail/c51b6cca-7709-40d8-afb2-2b035fe79803

## About the Role

* Strong proficiency in Python with experience building production-grade systems
 * Solid understanding of data structures, algorithms, and core computer science fundamentals
 * Experience designing and building distributed systems at scale
 * Hands-on experience with concurrency, multithreading, or asynchronous programming
 * Experience with microservices architecture and API design
 * Practical experience with Kubernetes and containerized deployments
 * Ability to design systems that account for failure scenarios, scaling challenges, and performance trade-offs, * Experience with AWS or similar cloud platforms
 * Familiarity with event-driven systems such as Kafka or pub/sub architectures
 * Experience with caching strategies such as Redis
 * Exposure to observability tools for logging, monitoring, and tracing
 * Some familiarity with AI or ML systems, LLMs, or agent-based architectures

## Description

* Design and build scalable backend services in Python for AI and agent platforms
 * Architect systems that handle high concurrency, large volumes of data, and low-latency requirements
 * Build and operate microservices deployed on Kubernetes
 * Design systems with reliability, fault tolerance, and observability as core principles
 * Plan for horizontal scalability to support rapid growth and increasing usage
 * Work on asynchronous processing, distributed workflows, and event-driven architectures
 * Collaborate with cross-functional teams to define platform capabilities that support AI use cases
 * Continuously improve system performance, reliability, and cost efficiency


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