AI Engineer

Job Description

What You'll Own :

AI Infrastructure Architecture

• 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
  
  

• LOOKING FOR CANDIDATES FROM TIER 1/2 INSTITUTES (Preferred).
• LOOKING FOR CANDIDATES FROM B2C DOMAIN (Preferred).
• 3 to 5 years building production systems handling >10k concurrent users
• Proven experience with async/event-driven architectures (not just REST APIs)
• 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
  
  

• Built systems integrating multiple LLM/AI models in production
• 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
• Has debugged production issues under high AI inference load
  
  

• 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
  
  

• Technical Assessment Intro + AI/ML focused technical discussion (60 minutes)
• System Design Architecture and scaling conversations (60 minutes)
• Final Round Cultural alignment and team interaction