Forward Deployed AI Engineer in Vancouver

A Forward Deployed AI Engineer is a production engineer who works embedded inside a client's enterprise, shoulder to shoulder with their teams, to make complex AI platforms work in real, messy organizational environments. You own outcomes: time-to-value, adoption, reliability, and scalability. Not delivery milestones. Outcomes.

The market is beginning to understand what leading technology companies have demonstrated: AI products fail not because the models are weak but because deployment is broken. The gap between a successful AI pilot and an AI capability that scales is bridged by engineers who can translate platform capability into measurable business value inside a real enterprise environment.

Forward Deployed AI Engineers form the execution spine of our Reinvention Deployment Engineering pods. We are building the largest FDE capability in the services industry. The engineers who join at this stage will define what the role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry.

Key Responsibilities

• Lead enterprise AI platform deployments across complex multi-stakeholder client environments — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — owning the full programme from architecture through adoption.
• Own programme-level delivery outcomes: time-to-value, reliability, adoption velocity, and scalability across multiple concurrent workstreams, with commercial metrics attached.
• Lead rapid experimentation at pace: drive ambiguous business problems to working production systems in days or weeks across complex enterprise environments.
• Architect and govern enterprise AI solutions across the full technology stack: identity, data, security, governance, platform layer, and multi-system workflow integration at programme scale.
• Shape AI reinvention strategy for client CTO, CFO, and CISO: build value architecture, ROI backlogs, use case prioritization frameworks, and multi-year AI adoption roadmaps.
• Define and publish reusable reinvention blueprints, patterns, and accelerators that scale across multiple client engagements and grow the FDE practice.
• Lead architecture design sessions, executive workshops, and code-with sessions with client engineering and C-suite leadership teams.
• Codify delivery learnings, failure patterns, and engineering standards that shape the FDE practice and enable the next generation of forward deployed engineers.

Basic Qualifications

• Bachelor’s degree or college diploma in a related field.
• Minimum of 7 years of engineering experience with cloud-native systems, including APIs, microservices, containerization, and serverless architectures.
• Minimum of 1 year of experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
• Minimum of 7 years of experience with AI platforms (OpenAI, Claude, Vertex AI, or open-source models), including building abstraction layers for multi-provider pipelines.
• Minimum of 7 years of experience leading software engineering teams, including delivery oversight, resource allocation, and professional development of direct reports.
• Demonstrated end-to-end delivery ownership in a client-embedded environment.
• Experience leading enterprise AI deployments across multi-stakeholder environments.
• Experience managing and developing engineering teams, including performance management and development planning.
• Experience working with enterprise AI platforms such as Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir.
• Experience defining enterprise AI architecture across identity, data, security, governance, and multi-system integrations.
• Experience working directly with senior stakeholders (CTO, CFO, CISO) on AI strategy and investment decisions.
• Experience leading executive workshops, architecture design sessions, or cross-functional technical engagements.