AI Engineer

Role Overview

As an AI Engineer, you will own the AI Infrastructure Architecture, designing and implementing asynchronous multi-agent orchestration and managing end-to-end latency from user messages to AI responses. You will build resilient inference pipelines, implement intelligent request routing, and migrate critical AI conversation flows from monoliths to dedicated services.

Responsibilities

• Design and implement asynchronous multi-agent orchestration
• Own end-to-end latency from user message to AI response
• Build resilient inference pipelines that gracefully degrade under load
• Implement intelligent request routing and load balancing for AI workloads
• Migrate critical AI conversation flow from monolith to dedicated services
• Implement WebSocket/streaming infrastructure for real-time chat
• Design circuit breakers and fallback strategies for AI model failures
• Build comprehensive observability for AI system performance
• Optimize credit data retrieval and caching strategies

Requirements

• 3 to 5 years building production systems handling >10k concurrent users
• Proven experience with async/event-driven architectures
• Hands-on experience scaling ML/AI inference in production
• Deep understanding of caching strategies (Redis, in-memory, CDN)
• Experience with message queues and real-time communication protocols
• Experience with AI model serving frameworks (TensorFlow Serving, Triton, etc.)
• Understanding of AI inference optimization (batching, caching, model quantization)
• Knowledge of conversation state management and context handling

Skills

• Python
• Redis
• TensorFlow Serving
• WebSocket
• System Design

Growth Path

• Direct impact on customer subscription retention through performance
• Exposure to cutting-edge AI infrastructure challenges
• Ownership of technical decisions affecting revenue-generating conversations
• Path to leading AI platform team as you scale