Job Specifications
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 Zilliz
Custom LLM deployments with fine-tuning, inference routing, and token optimization
Tool-calling and agent flows supporting complex, multi-step decisions
Reinforcement learning systems to evolve agent behavior over time
Streaming inference pipelines for voice, chat, and other live interactions
Multi-tenant ML infrastructure with robust data isolation and observability
Core Responsibilities
LLM, Retrieval, and Agent Systems
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
ML Engineering and Data Infrastructure
Build real-time and batch pipelines for ingestion, training, and inference
Deploy and monitor reinforcement learning systems
Own the ML model lifecycle across development, evaluation, deployment, and tuning
Drive continuous optimization across latency, cost, and performance
Systems Integration and Deployment
Build and maintain ML APIs and microservices using Docker and Kubernetes
Support streaming interaction layers including voice and WebSockets
Ensure production reliability, monitoring, and scale
Collaborate cross-functionally on platform-wide architecture and data contracts
You Should Have
7+ years of experience in ML, AI, or data engineering roles
Expert-level Python for backend, ML workflows, and orchestration
Experience with modern LLM frameworks such as LangChain or LangGraph
Deep knowledge of vector databases and retrieval systems
Production experience with reinforcement learning
Comfort with distributed systems, Docker, and Kubernetes
Experience building and maintaining streaming or real-time pipelines
A track record of shipping complex systems that work in production
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
CRM 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.
About the Company
Raynmaker is redefining sales for small and medium-sized businesses with an enterprise-grade, end-to-end autonomous platform. Powered by our proprietary RaynBrain™ sales insight engine, Raynmaker converts leads into customers by handling every step of the sales journey, from first contact to closed deal, with an AI agent that books appointments, takes payments, and answers questions 24/7, all in a lifelike, brand-aware, context-sensitive human voice. For SMBs, it’s like hiring a world-class sales team on demand.
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