AI Engineer
Job Description
What You'll Own :
AI Infrastructure Architecture
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
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