Ai Forward Deployed Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Forward Deployed Engineer based in Canada.
Join an innovative engineering team building the next generation of AI-powered tools, services, and integrations. In this role, you will design and operate production-ready services that enable AI agents to interact safely and reliably with external systems. You will develop reusable capabilities for meeting participation, web browsing, search, email, payments, and automated workflows. Working across product engineering, platform architecture, and applied AI, you will help transform emerging agent use cases into practical solutions. You will also build internal tools that improve employee productivity and external integrations that simplify customer experiences. This is an opportunity to shape the architecture of an evolving AI platform while solving meaningful real-world engineering challenges.
AccountabilitiesAs an AI Forward Deployed Engineer, you will take ownership of developing reliable AI infrastructure and agent capabilities, collaborating with cross-functional teams to turn innovative ideas into scalable production services. Your responsibilities will include:
Design, build, deploy, and operate production-grade services that enable AI agents to perform tasks reliably.
Develop internal tools and applications that improve employee productivity and streamline everyday workflows.
Build external integrations, connectors, and tools that simplify how customers interact with and integrate services into AI-powered applications.
Develop reusable agent primitives and capabilities, including meeting participation, web browsing, search, browser automation, email, and payment services.
Establish reliable interfaces, workflows, and protocols for agent tool usage and orchestration.
Collaborate closely with product and engineering teams to translate emerging AI agent use cases into practical, maintainable services.
Strengthen platform safety, observability, testing, security, and operational reliability.
Contribute to the overall architecture, technical strategy, and evolution of the agent enablement platform.
Ensure that AI agents can interact with external systems securely, manage permissions appropriately, and handle actions with real-world consequences.
Requirements
The ideal candidate is a hands-on software engineer with strong production engineering experience, practical knowledge of applied AI, and the ability to turn ambiguous technical challenges into reliable products. You should bring:
Strong software engineering experience, including ownership and maintenance of production systems.
Practical experience working with Large Language Models (LLMs), AI agents, tool calling, and agent orchestration.
A solid understanding of designing reliable, scalable, and maintainable software services.
Good technical judgment regarding system reliability, security, access permissions, and external side effects.
The ability to navigate ambiguous problems, evaluate emerging technologies, and turn early-stage ideas into robust engineering solutions.
Strong problem-solving and analytical skills, with a practical and hands-on approach to development.
Clear communication and the ability to collaborate effectively with product and engineering teams.
Experience with agent frameworks and runtimes such as Hermes Agent or OpenClaw is considered a plus.
An interest in developing secure, dependable AI-powered products and shaping the future of agent-based applications.
Benefits
Fully remote opportunity: Work remotely while collaborating with an international engineering team.
Cutting-edge AI projects: Contribute to the development of advanced AI agent capabilities, integrations, and production services.
Technical ownership: Help shape platform architecture, engineering standards, and the direction of an evolving AI enablement ecosystem.
Continuous learning: Work with emerging AI technologies, LLMs, agent frameworks, and modern software engineering practices.
Meaningful impact: Build tools that improve employee productivity and make AI-powered integrations more accessible to customers.
Collaborative environment: Partner with product and engineering teams to solve complex technical challenges and bring new ideas into production.
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