AI Infrastructure Platform Engineer (Python)

Bayside Solutions

Austin, TX, United States

11 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Compensation

$114,400.0 - $135,200.0

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Artificial Intelligence Amazon Web Services Big Data Data Structures Distributed Systems Fault Tolerance Python (Programming Language) Redis Data Logging Multithreading Large Language Models Grafana

+12 more

Concurrency Caching Backend Event Driven Architecture Build Management Containerization Kubernetes Apache Kafka Machine Learning Operations Asynchronous Programming Api Design Microservices

Job 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

Requirements

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