AI Engineer - Everest

Core 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

What We're Looking For

• 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.

Bonus Points

• 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.