Forward Deployed AI Engineer - Senior Manager

Role description

Key responsibilities

• Own account-level AI transformation from platform selection and architecture through adoption and ongoing iteration.
• Lead deployments of enterprise AI platforms across complex, multi-stakeholder client environments.
• Own deployment quality, time-to-value, reliability, adoption velocity, scalability, and ROI across concurrent workstreams.
• Translate executive intent and ambiguous business problems into production AI systems; rapidly prototype and deploy workflows, agents, copilots, and automations.
• Define enterprise architecture standards and governance spanning identity, data, security, platforms, integrations, and AI lifecycle management.
• Architect solutions involving prompts, workflows, APIs, orchestration, enterprise knowledge, and privacy.
• Shape AI investment strategy and multi-year adoption roadmaps with client CTO, CFO, and CISO stakeholders.
• Create reusable blueprints, patterns, accelerators, and playbooks; grow the FDE practice and enable engineers.
• Lead executive and board workshops, architecture sessions, code-with sessions, and cross-practice reviews.
• Work with clients and internal teams to shape use cases and solutions, bridging product, engineering, data, and business stakeholders.
• Integrate LLMs and AI services with enterprise platforms such as SAP, ServiceNow, Salesforce, data platforms, and internal systems.
• Travel may be required at 25%–75%, depending on business need and client requirements.

Required qualifications

• Bachelor’s degree or college diploma in a related field.
• 10–15 years of engineering experience with cloud-native systems, including APIs, microservices, containerization, and serverless architectures.
• At least 8 years of software engineering experience using Java, TypeScript/JavaScript, or Python; at least 8 years integrating APIs and building distributed production systems.
• At least 1 year designing and deploying production agentic solutions, including agents, orchestration, context engineering, RAG, or workflows.
• At least 1 year applying AI-native and agentic development practices across the SDLC, including spec-driven development and AI coding agents.
• At least 1 year working with AI platforms such as OpenAI, Claude, Vertex AI, or open-source models, plus experience with enterprise AI platforms.
• Experience building abstraction layers for multi-provider AI pipelines.
• 10–15 years leading and developing software engineering teams, including delivery oversight, resource allocation, performance management, and staff development.
• At least 8 years of client-facing collaboration and technical engagement, and at least 8 years translating business needs into enterprise technical designs.
• Demonstrated ownership of client-embedded enterprise AI deployments and experience working directly with enterprise clients.
• Experience defining enterprise AI architecture standards across identity, data, security, governance, integrations, and lifecycle management.
• Experience with executive stakeholders, executive workshops, reusable deployment frameworks, and engineering standards or practice models.

Compensation

• Annual base salary range stated for British Columbia, Ontario, and New Brunswick: CAD 142,050 to CAD 292,000.
• Compensation varies by office location, role, skills, and experience; the range is a guideline and offers may exceed it.

Travel

• Travel may be required and may vary from 25% to 75%, depending on business need and client requirements.