AI Engineer - Everest

Infinity

10d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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