RaYnmaker

AI Engineer

• Design and optimize production-grade RAG systems
• Build ranking, scoring, and routing models for live inference
• Architect tool-calling flows, agent memory, and multi-turn reasoning
• Optimize token usage, caching, and cost-performance tradeoffs
• Maintain and enrich vector knowledge bases
• Build real-time and batch pipelines for ingestion, training, and inference
• Deploy and monitor reinforcement learning systems
• Build and maintain ML APIs and microservices using Docker and Kubernetes
• Support streaming interaction layers including voice and WebSockets
• Collaborate cross-functionally on platform-wide architecture and data contracts

Artificial Intelligence Engineer

• Design, build, and optimize the pipelines and agent systems that drive live customer interactions
• Build ranking, scoring, and routing models for live inference
• Architect tool-calling flows, agent memory, and multi-turn reasoning
• Optimize token usage, caching, and cost-performance tradeoffs
• Build and maintain ML APIs and microservices using Docker and Kubernetes
• Collaborate cross-functionally on platform-wide architecture and data contracts

• Design, build, and optimize the pipelines and agent systems that drive live customer interactions
• Develop RAG pipelines using Milvus, Weaviate, Pinecone, or Zilliz
• Create custom LLM deployments with fine-tuning and inference routing
• Architect tool-calling and agent flows supporting complex, multi-step decisions
• Build reinforcement learning systems to evolve agent behavior over time
• Stream inference pipelines for voice, chat, and other live interactions
• Develop multi-tenant ML infrastructure with robust data isolation
• Collaborate with engineering leadership to transition ideas from whiteboard to production

• Architect and scale the core intelligence behind the platform
• Design, build, and optimize the pipelines and agent systems that drive live customer interactions
• Partner with engineering leadership to take ideas from whiteboard to production quickly
• Design and optimize production-grade RAG systems
• Build ranking, scoring, and routing models for live inference
• Deploy and monitor reinforcement learning systems
• Build and maintain ML APIs and microservices using Docker and Kubernetes