VP, AI Engineering & Agent Platforms

About this role

Opportunity Overview:

Reporting to the Chief Digital & Technology Officer, the Vice President of AI Engineering & Agent Platforms will lead the teams responsible for AI platform engineering, agent platforms, agent runtime systems, skills and prompt lifecycle management and framework, AI infrastructure, MLOps/LLMOps, and forward deployed AI engineering.

This role partners closely with the Chief Data & AI Officer, who owns Cohere's AI strategy, model development, evaluation frameworks, prompt design and governance, skills requirements and behavior, knowledge management frameworks, and data science functions. The VP of AI Engineering & Agent Platforms is responsible for operationalizing, scaling, deploying, and running those capabilities across Cohere's products and customer environments.

This leader will build the platforms, engineering systems, and deployment capabilities that enable Cohere to rapidly deliver AI-powered solutions while maintaining the reliability, security, and compliance required in healthcare.

What You'll Do:

Build and Scale Our AI Platform

Lead the engineering organization responsible for the foundational platforms and services that power Cohere's AI ecosystem.

Responsibilities include:

• AI infrastructure and runtime platforms

• Agent orchestration, workflow, and execution services

• Document processing and knowledge ingestion pipelines

• MLOps and LLMOps capabilities

• AI observability, monitoring, and reliability

• Partnership with core teams to build AI native Developer platforms and engineering productivity tools

• Build and evolve Cohere's enterprise agent platform, enabling teams to rapidly develop, evaluate, deploy, govern, and operate AI agents at scale.

Lead Agent Engineering

Build the frameworks, services, and reusable capabilities that enable teams to rapidly develop, test, deploy, and operate secure, observable, and production-ready AI-powered solutions.

Areas of focus include:

• Agent architectures, orchestration, and runtime frameworks

• Multi-agent systems and workflow automation

• Skills management and reusable action frameworks

• Evaluation, testing, and agent observability infrastructure

• Human-in-the-loop and supervised AI workflows

• Enterprise integrations and action surfaces

• Partnership in skills design with data science

Design and scale the engineering systems used to build, manage, deploy, and govern reusable agent skills across healthcare workflows.

Lead Prompt and Skills Lifecycle Operations

Establish the platforms and operational capabilities required to manage AI behavior at scale.

Responsibilities include:

• Prompt lifecycle management

• Prompt deployment and versioning