AI Infrastructure Platform Engineer (Python)
Bayside Solutions
Austin, TX, United States
8 days ago
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Role details
Contract type
Contract
Employment type
Full-time (> 32 hours)
Compensation
$114,400 - $135,200
Working hours
Regular working hours
Job source
Tech stack
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
+14 more
Grafana
Concurrency
Caching
Backend
Agentic-AI
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
About the company
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