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# AI Infrastructure Platform Engineer (Python)

- **Company:** Bayside Solutions
- **Location:** Austin, TX, United States (Remote available)
- **Salary:** $114,400 - $135,200
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Big Data, Data Structures, Distributed Systems, Fault Tolerance, Python (Programming Language), Redis, AI Infrastructure, Data Logging, Multithreading, Large Language Models, Grafana, Concurrency, Caching, Backend, Agentic-AI, Event Driven Architecture, Build Management, Containerization, Kubernetes, Apache Kafka, Machine Learning Operations, Asynchronous Programming, Api Design, Microservices
- **Published:** November 4, 2026
- **Apply:** https://www.dice.com/job-detail/6ae93dea-294b-44fb-a95c-b236dcaba58b

## 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