Everest is hiring a Backend Engineer, AI Systems to build the AI-native infrastructure and agentic systems that power our next-generation executive support platform. You'll architect core systems that balance deterministic logic with stochastic outcomes while collaborating directly with the executive assistants who rely on them.

What You'll Do

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 like text, audio, video, and images.
Optimize latency, extensibility, reliability, and inference cost of multi-agent pipelines.
Collaborate directly with internal Executive Assistants to pressure-test workflows in the real world.
Help make clear decisions about when to use LLMs versus traditional systems.

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 or 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 Have

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