Forward Deployed AI Engineer
Role Overview
A production engineer embedded with client teams to make complex AI platforms work in enterprise environments.
Own outcomes including time-to-value, adoption, reliability, and scalability; the role is described as neither consulting, project delivery, nor research.
Key Skills for This Role
Full Job Posting
Role overview
A production engineer embedded with client teams to make complex AI platforms work in enterprise environments.
Own outcomes including time-to-value, adoption, reliability, and scalability; the role is described as neither consulting, project delivery, nor research.
Responsibilities
- Lead enterprise AI platform deployments from architecture through adoption across complex client environments.
- Own programme-level outcomes across concurrent workstreams, including time-to-value, reliability, adoption velocity, scalability, and commercial metrics.
- Drive ambiguous business problems to production systems through rapid experimentation.
- Architect and govern solutions across identity, data, security, governance, platforms, and multi-system workflows.
- Shape AI strategy, value architecture, ROI backlogs, use-case prioritization, and multi-year adoption roadmaps with client executives.
- Publish reusable blueprints, patterns, and accelerators; lead architecture sessions, executive workshops, and code-with sessions.
- Codify delivery learnings, failure patterns, and engineering standards for the FDE practice.
Required qualifications
- Bachelor’s degree or college diploma in a related field.
- At least 7 years of engineering experience with cloud-native systems, APIs, microservices, containerization, and serverless architectures.
- At least 1 year designing and deploying production agentic solutions, including agents, orchestration, context engineering, RAG, or workflows.
- At least 7 years of AI platform experience, including OpenAI, Claude, Vertex AI, or open-source models, and building multi-provider abstractions.
- At least 7 years leading software engineering teams, including delivery oversight, resource allocation, and direct-report development.
- Demonstrated end-to-end delivery ownership in a client-embedded environment and experience leading enterprise AI deployments.
- Experience with enterprise AI architecture, team development, senior stakeholders, and executive workshops or architecture sessions.
Compensation
- Annual base salary range: CAD 100,300 to CAD 216,700 for British Columbia, Ontario, and New Brunswick.
- The employer states compensation varies by office location, role, skills, and experience; the range is a guideline for full-time employment.
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