Forward Deployed Engineer, Agentic Platform

About the Role

Cohere is looking for Software Engineers with Applied AI experience who can own the design, build, and deployment of agentic workflows powered by Large Language Models (LLMs), from early prototypes to production-grade AI agents, to deliver concrete business value in enterprise workflows. As a Forward Deployed Engineer on the North platform team, you’ll work closely with customers on real-world business problems, building reliable, observable, safe, and auditable agent workflows that integrate LLMs with tools, APIs, and data sources.

*Note: Between 20%–40% travel anticipated to work on-site with customers and partners.*

Responsibilities

• Work closely with enterprise customers to translate high-value, ambiguous business problems into well-framed agentic workflows with clear success criteria and evaluation methodologies.
• Lead the design, build, and delivery of LLM-powered agents that reason, plan, and act across tools, APIs, and sensitive enterprise data sources with enterprise-grade reliability and performance.
• Build and ship features for North, Cohere's AI workspace platform, working across the full product lifecycle from conceptualisation through production.
• Take ownership of scoping and shaping use cases end-to-end, flexing into whatever technical area the problem demands (including frontend) to drive the most effective solution.
• Contribute to shared frameworks and patterns that enable consistent, high-quality delivery across customers and teams.
• Drive clarity in ambiguous situations, build alignment, and raise engineering quality across the organization.
• Travel up to 20%–40% to work on-site with customers and partners.

Qualifications

Required

• Hands-on experience building and deploying production-grade software in Python; writing clean, testable, observable, scalable code.
• Experience building and deploying highly performant RAG and agentic applications, including agents that plan and execute multi-step tasks using patterns like ReAct or Plan-and-Execute.
• Deep familiarity with the LLM stack: frontier models, vector databases, and orchestration frameworks.
• Proven ability to build robust evaluation frameworks to measure agent accuracy, safety, and latency.
• Experience working directly with customers and leading technical discussions with enterprise stakeholders, translating ambiguous business needs into concrete technical specs.
• Experience owning the full scope of a use case end-to-end.
• Ability to thrive in fast-paced and ambiguous environments and execute well even when priorities shift.

Preferred

• Experience setting architectural standards for AI and agentic systems across distributed teams.
• Experience flexing into unfamiliar technical areas, such as frontend, when required.
• Exposure to regulated or sensitive industry environments (finance, healthcare, telecoms).
• Experience with enterprise security, compliance, or auditability requirements for AI systems.