Artificial Intelligence Engineer

Where Neuroscience Meets Agentic AIAbout RaynmakerWe’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 RoleWe’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 BuildRAG 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 observabilityCore ResponsibilitiesLLM, Retrieval, and Agent SystemsDesign 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 basesML Engineering and Data InfrastructureBuild 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 performanceSystems Integration and DeploymentBuild 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 contractsYou Should Have7+ 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 productionNice to HaveFamiliarity 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 HubSpotWhy RaynmakerHigh 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.This Organization Participates in E-VerifyThis employer participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.If E-Verify cannot confirm that you are authorized to work, this employer is required to give you written instructions and an opportunity to contact Department of Homeland Security (DHS) or Social Security Administration (SSA) so you can begin to resolve the issue before the employer can take any action against you, including terminating your employment.Employers can only use E-Verify once you have accepted a job offer and completed the Form I-9.