Über
RaynBrain , the groundbreaking agentic AI platform designed for intricate conversations. Harnessing the power of machine learning, neuroscience, and forensic linguistics, RaynBrain empowers autonomous systems to interpret, adapt, and act in real time. These systems transform raw leads into tangible revenue without the need for scripts or static flows. Raynmaker streamlines traditional sales stacks, enabling small teams to operate efficiently, enhance conversion rates, and optimize lead management. Our AI excels in listening, reasoning, and closing deals. The Role We are seeking a Senior AI/ML Engineer to be a pivotal force in architecting and scaling the core intelligence of our platform. This role encompasses systems design, ML engineering, and LLM integration, sitting at the crucial intersection of infrastructure and applied AI. Your mission will be to design, build, and optimize the pipelines and agent systems that facilitate live customer interactions. This includes developing retrieval-augmented generation (RAG) systems, scoring models, vector search, real-time streaming inference, memory management, and reinforcement learning systems - all deployed in a production environment and designed for scalability. You will collaborate closely with our engineering leadership to rapidly transform innovative concepts into operational solutions while making vital decisions regarding performance, cost efficiency, and reliability. What You'll Build RAG pipelines utilizing Milvus, Weaviate, Pinecone, or Zilliz Custom LLM deployments with fine-tuning, inference routing, and token optimization Agent flows and tool-calling systems to support complex multi-step decisions Reinforcement learning systems to refine agent behavior over time Streaming inference pipelines for real-time interactions including voice and chat A multi-tenant ML infrastructure ensuring robust data isolation and observability Core Responsibilities LLM, Retrieval, and Agent Systems Design and optimize production-grade RAG systems Develop ranking, scoring, and routing models for live inference Create tool-calling flows, agent memory, and multi-turn reasoning architectures Optimize token usage, caching, and evaluate cost-performance trade-offs Maintain and enhance vector knowledge bases ML Engineering and Data Infrastructure Construct real-time and batch pipelines for data ingestion, training, and inference Deploy and monitor reinforcement learning systems Oversee the ML model lifecycle from development to evaluation and deployment Drive continuous optimization across latency, cost, and performance Systems Integration and Deployment Develop and maintain ML APIs and microservices using Docker and Kubernetes Support streaming interaction layers, including voice and WebSockets Ensure production reliability, monitoring, and scalability Collaborate cross-functionally on platform-wide architecture and data contracts You Should Have 7+ years of experience in ML, AI, or data engineering roles Proven expertise in Python for backend development and ML workflows Experience with contemporary LLM frameworks such as LangChain or LangGraph In-depth knowledge of vector databases and retrieval systems Hands-on experience with reinforcement learning in production Familiarity with distributed systems, Docker, and Kubernetes Experience in building and maintaining streaming or real-time pipelines A record of delivering complex systems effective in production environments Nice to Have Familiarity with AWS ML stack including SageMaker or Bedrock Experience with Kafka, Kinesis, or Pulsar Knowledge of model compression, quantization, or accelerated inference Background in CRM or sales technology such as Salesforce or HubSpot Why Raynmaker High Impact : We are focused on democratizing technology for the 99% of businesses often overlooked by legacy software. Your contributions will empower small teams with fast, cost-effective, and capable technology. Hard Problems : We tackle significant challenges in real-time inference, agent coordination, and scalable autonomy, going beyond merely wrapping APIs. Applied Intelligence : Our work merges machine learning with insights from neuroscience and forensic linguistics to understand not just the words spoken, but the underlying motivations and emotions. You'll design agents capable of recognizing hesitant speech, sentiment fluctuations, and objection timing, adjusting strategies dynamically based on behavioral cues. Deep Ownership : You'll take full ownership of the architecture and systems you build, shaping every aspect from conception to completion. This is not mere research; it is the development of production-grade intelligence designed to resolve genuine challenges for real businesses every day. If you are driven by the desire to make such an impact, we are eager to hear from you. This Organization Participates in E-Verify This 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 provide you with written instructions and an opportunity to contact the Department of Homeland Security (DHS) or Social Security Administration (SSA) to resolve the issue before any action can be taken against you, including termination of employment. Employers can only use E-Verify once you have accepted a job offer and completed the Form I-9.
Sprachkenntnisse
- English
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