AI Engineer - Everest

Everest is reshaping how elite executive assistance is delivered to founders, entrepreneurs, executives, and high-net-worth individuals. Our clients expect exceptional service: proactive, strategic, discreet, and seamless. We operate with the adaptability of a high-performing technology organization: iterating quickly, learning from feedback, and improving our systems at speed. We’re collaborative, supportive, and focused on sustainable excellence.

• →

  Design and implement backend systems that power agentic workflows across LLM, deterministic, and hybrid pipelines.

• →

  Own and evolve core infrastructure like context memory, orchestration layers, and prompt routing systems.

• →

  Design composable multimodal systems that dynamically execute workflows from unstructured inputs (text, audio, video, images).

• →

  Optimize latency, extensibility, reliability, and inference cost of multi-agent pipelines.

• →

  Collaborate with stakeholders to pressure-test workflows in the real world.

• →

  Help us make clear decisions about when to use LLMs vs. traditional systems—and how to do both well.

• →

  Develop and improve GraphRAG-based knowledge retrieval systems using Neo4j

• →

  Integrate and orchestrate LLM calls for document processing workflows

• 5+ years of experience in backend software engineering, preferably in Go or similar systems languages.

• Shipped agentic LLM systems to production (not prototypes, not demos).

• Built real-time systems, distributed async queues, or performance-critical services.

• Deep understanding of prompt engineering, token budgeting, and context management.

• Strong intuition for when to use AI—and when not to.

• Thrive in small teams with high trust and high ownership.

• Experience with RAG, embedding stores, and vector DBs.

• Experience designing evals for AI agents and workflows

• Familiarity with tool orchestration frameworks.

• Understanding of the architectural tradeoffs of agentic systems, RAG, MCP, memory, and orchestrations.

• Know how to work with (and around) the limitations of cutting-edge LLM technologies.

• Background in AI safety, observability, or human-in-the-loop workflows.

• Prefer building systems that are simple, scalable, and "good enough," without sacrificing maintainability or future flexibility.

• Are fluent in small-team dynamics: high trust, low ego, shared accountability.