Machine Learning Engineer

Responsibilities:

• 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.
• Optimise credit data retrieval and caching strategies.

Requirements:

• 3+yearsbuilding 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 models serving frameworks (TensorFlow Serving, Triton, etc. ).
• Understanding of AI inference optimisation (batching, caching, model quantisation).
• 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 an AI platform team as you scale.