Cohere
Mid
Forward-deployed engineer building enterprise-grade LLM agents on Cohere's North platform, on-site with customers 20-40% of the time
This Forward Deployed Engineer role sits on Cohere's Agentic Platform team, building and shipping production-grade LLM-powered agentic workflows for North (Cohere's enterprise AI workspace) directly with enterprise customers in regulated sectors like finance, healthcare, and telecom. The JD asks for hands-on Python engineering, experience building and deploying RAG/agentic applications with patterns like ReAct or Plan-and-Execute, and the ability to build robust evaluation frameworks for agent accuracy, safety, and latency. Expect questions that test both technical depth (LLM stack, orchestration, evaluation rigor) and customer-facing skill (translating ambiguous enterprise problems into scoped technical specs), with 20-40% travel to client sites.
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What this interview tests
- Production-grade agentic workflow design (ReAct, Plan-and-Execute patterns)
- RAG applications and LLM stack (frontier models, vector databases, orchestration frameworks)
- Building evaluation frameworks for agent accuracy, safety, and latency
- Translating ambiguous enterprise problems into scoped technical specs
- Customer-facing technical leadership in regulated industries (finance, healthcare, telecom)
- Full-lifecycle ownership from prototype to production, flexing across the stack
Common question themes
Walk through an agentic workflow you built end-to-end using ReAct or Plan-and-Execute
How would you design an evaluation framework to measure agent accuracy, safety, and latency
Describe translating an ambiguous customer business problem into a technical spec with success criteria
How do you make an LLM agent reliable, observable, and auditable for a regulated customer
Tell me about a time you had to flex into an unfamiliar technical area (e.g. frontend) to unblock a project
How do you lead a technical discussion with skeptical enterprise engineering stakeholders