Job Title: Artificial Intelligence Engineer Company Name: Raynmaker Job Details: $135k-$175k/yrRemoteFull,Time Job Url: https://hiring.cafe/viewjob/243h4a5pzcykcpgi Job Description: Posted 15h agoArtificial Intelligence Engineer@ RaynmakerView All JobsWebsiteUnited States$135k-$175k/yrRemoteFull TimeResponsibilities:Design pipelines, Build models, Maintain APIsRequirements Summary:7+ years in ML/AI or data engineering; expert Python; experience with LLM frameworks; vector databases; RL; distributed systems; streaming pipelines; production-grade systems.Technical Tools Mentioned:Python, LangChain, LangGraph, Docker, Kubernetes, Milvus, Weaviate, Pinecone, Zilliz, WebSockets, Kafka, Kinesis Where Neuroscience Meets Agentic AI About Raynmaker We’re building RaynBrain, the first agentic AI platform for complex conversations. Grounded in machine learning, neuroscience, and forensic linguistics, RaynBrain powers autonomous systems that interpret, adapt, and act in real time. These systems turn raw leads into revenue without scripts, static flows, or human handoffs. Enterprise power without the bloat. Raynmaker helps small teams move faster, convert more, and never waste another lead. We replace the complexity of traditional sales stacks with AI that listens, reasons, and closes. The Role We’re hiring a Senior AI/ML Engineer to architect and scale the core intelligence behind our platform. This role spans systems design, ML engineering, and LLM integration. It sits at the intersection of infrastructure and applied AI. You will design, build, and optimize the pipelines and agent systems that drive live customer interactions. That includes retrieval-augmented generation (RAG), scoring models, vector search, real-time streaming inference, memory management, and reinforcement learning systems. All of it is deployed in production and built to scale. You will partner with engineering leadership to take ideas from whiteboard to production quickly and own key decisions around performance, cost efficiency, and reliability. What You'll Build RAG pipelines using Milvus, Weaviate, Pinecone, or ZillizCustom LLM deployments with fine-tuning, inference routing, and token optimizationTool-calling and agent flows supporting complex, multi-step decisionsReinforcement learning systems to evolve agent behavior over timeStreaming inference pipelines for voice, chat, and other live interactionsMulti-tenant ML infrastructure with robust data isolation and observability Core Responsibilities LLM, Retrieval, and Agent Systems Design and optimize production-grade RAG systemsBuild ranking, scoring, and routing models for live inferenceArchitect tool-calling flows, agent memory, and multi-turn reasoningOptimize token usage, caching, and cost-performance tradeoffsMaintain and enrich vector knowledge bases ML Engineering and Data Infrastructure Build real-time and batch pipelines for ingestion, training, and inferenceDeploy and monitor reinforcement learning systemsOwn the ML model lifecycle across development, evaluation, deployment, and tuningDrive continuous optimization across latency, cost, and performance Systems Integration and Deployment Build and maintain ML APIs and microservices using Docker and KubernetesSupport streaming interaction layers including voice and WebSocketsEnsure production reliability, monitoring, and scaleCollaborate cross-functionally on platform-wide architecture and data contracts You Should Have 7+ years of experience in ML, AI, or data engineering rolesExpert-level Python for backend, ML workflows, and orchestrationExperience with modern LLM frameworks such as LangChain or LangGraphDeep knowledge of vector databases and retrieval systemsProduction experience with reinforcement learningComfort with distributed systems, Docker, and KubernetesExperience building and maintaining streaming or real-time pipelinesA track record of shipping complex systems that work in production Nice to Have Familiarity with AWS ML stack including SageMaker or BedrockExperience with Kafka, Kinesis, or PulsarKnowledge of model compression, quantization, or accelerated inferenceCRM or sales tech background such as Salesforce or HubSpot Why Raynmaker High Impact: We are building for the 99 percent of businesses left behind by legacy software. Your work will help small teams win with tech that is fast, affordable, and deeply capable.Hard Problems: We are solving real-time inference, agent coordination, and scalable autonomy, not just wrapping APIs.Applied Intelligence: We combine machine learning with neuroscience and forensic linguistics to model not just what people say but how and why they say it. You'll build agents that detect hesitation patterns, sentiment shifts, and objection timing - then adapt strategy in real time based on behavioral cues, not just keywords.Deep Ownership: You will shape architecture and systems from end to end, not just optimize what someone else scoped. This isn’t research for research's sake. This is production-grade intelligence solving real problems for real businesses, every single day. If that’s the kind of impact you want, we’d love to meet you.