Everest
Tech / AI / Software
AIEngineerEverest
“AI Engineer - Everest at Everest. Skills: AI Engineer, backend software engineering, agentic LLM systems, LLM, GraphRAG, prompt engineering. 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”
Industry & Context.
make clear decisions about when to use LLMs vs. traditional systems—and how to do both well
What They're Looking For.
Must Have
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, intuition for when to use AI—and when not to
Nice to Have
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
What You'll Do.
Design and implement backend systems that power agentic workflows across LLM
Own and evolve core infrastructure like context memory
and prompt routing systems
Design composable multimodal systems that dynamically execute workflows from unstructured inputs (text
and inference cost of multi-agent pipelines
Develop and improve GraphRAG-based knowledge retrieval systems using Neo4j
Integrate and orchestrate LLM calls for document processing workflows
How You'll Work.
Team & Collaboration
Collaborate with stakeholders to pressure-test workflows in the real world; Thrive in small teams with high trust and high ownership; fluent in small-team dynamics: high trust, low ego, shared accountability
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