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

Technical Requirements :

- 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

AI-Specific Expertise :

- 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

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

Interview Process :

1. Technical Assessment Intro + AI/ML focused technical discussion (60 minutes)

2. System Design Architecture and scaling conversations (60 minutes)

3. Final Round Cultural alignment and team interaction