Role Description

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
• 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.

Qualifications

• 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

Requirements

• 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

Benefits

• 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.