Cohere
Forward Deployed Engineer, Agentic Platform
About this role
Cohere seeks a Forward Deployed Engineer to design and build enterprise-grade AI agents on the North platform, serving as a technical bridge between the product team and customers in high-stakes industries. You'll own the full lifecycle of agentic workflows—from prototyping to production—integrating LLMs with enterprise tools and data while traveling 20-40% to work directly with clients.
What you'll do
- Partner with enterprise customers to translate business problems into well-scoped agentic workflows with clear success metrics
- Design, build, and deploy production-grade LLM-powered agents that reason and act across APIs, tools, and sensitive data sources
- Develop features for the North AI workspace platform across the entire product lifecycle
- Own end-to-end use case scoping and execution, including technical decisions across frontend and backend as needed
- Build evaluation frameworks to measure agent reliability, safety, accuracy, and performance
- Travel to customer sites and contribute to organizational engineering standards and patterns
What they're looking for
- Production Python development with clean, testable, observable code
- RAG and agentic systems (ReAct, Plan-and-Execute patterns)
- LLM stack expertise (frontier models, vector databases, orchestration frameworks)
- Enterprise customer engagement and technical leadership
- Evaluation framework design and testing methodologies
- Full-stack technical ownership and ambiguous problem-solving
- API integration and tool orchestration
- System design for reliability and scalability
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Cohere
Cohere builds enterprise AI systems and large language model infrastructure, including audio inference optimization, agentic AI workflows, and petabyte-scale data infrastructure for model training. The company is hiring software engineers, infrastructure specialists, forward-deployed engineers to work with enterprise customers, and IT support staff.
- Website
- cohere.com
Likely interview questions
- Walk us through a production RAG or agentic application you built end-to-end. What evaluation framework did you use to measure its performance beyond trial-and-error?
- Tell us about a time you translated an ambiguous enterprise business problem into concrete technical specifications for an LLM-powered solution. How did you scope it?