DevOps - AI Practice Lead

F12.net•Edmonton, AB

38d•Onsite

About The Position

The AI Practice Lead is responsible for building, scaling, and operating F12’s AI services practice as a revenue-generating business unit. This role blends strategy, customer engagement, productization, and commercial ownership, with accountability for Professional Services (PS) delivery, Managed services (MRR growth), AI product resale strategy, and R&D and innovation pipeline development. You will take emerging AI capabilities and turn them into repeatable, scalable offerings that drive customer value and recurring revenue.

Requirements

Professional Services (PS) delivery
Managed services (MRR growth)
AI product resale strategy
R&D and innovation pipeline development
Emerging AI capabilities
Repeatable, scalable offerings
Customer value and recurring revenue
AI services and solutions (advisory, implementation, managed, resale)
Pricing, packaging, and commercial models
Practice-level performance including revenue growth, margin, and profitability
Customer workshops, discovery sessions, and advisory engagements
Business challenges into scalable AI-driven solutions
Trusted advisor on AI adoption, use cases, and ROI realization
Pre-sales solutioning and proposal development
AI implementation engagements (POCs, pilots, deployments)
Repeatable delivery frameworks and methodologies
On-demand service offerings that are easy to scope, sell, and deliver
Delivery quality and measurable business outcomes
Recurring AI service offerings
AI optimization and tuning
Automation lifecycle management
Copilot adoption and enablement
Project-based work into long-term managed services
AI as a core attach opportunity across the client base
Third-party AI platforms and products for resale
Resale and partner strategy aligned to F12’s model
Products with services to create bundled MRR offerings
Vendor relationships, pricing, and margin optimization
Sales on attach, upsell, and positioning strategies
Structured R&D initiatives to evaluate emerging AI technologies
Experiments, pilots, and internal prototypes
Validated use cases into production-ready services and IP
Pipeline of innovation feeding new PS and MRR offerings
Trends in generative AI and automation
AI solutions, automation workflows, and integrations
Best practices for prompting, model usage, and solution design
Internal enablement assets including playbooks and training
Collaboration across CFO, CRO, and CTO portfolios
Commercially viable, operationally scalable, and technically aligned solutions
High-performing AI practice team
AI initiatives with business goals and customer outcomes

Nice To Haves

AI services
AI product resale
R&D and innovation pipeline development
Emerging AI capabilities
Repeatable, scalable offerings
Customer value and recurring revenue
AI services and solutions
Commercial ownership
Customer engagement
Productization
AI adoption
ROI realization
AI implementation engagements
Delivery frameworks and methodologies
Recurring AI service offerings
AI optimization and tuning
Automation lifecycle management
Copilot adoption and enablement
AI product resale and commercialization
Third-party AI platforms and products
Resale and partner strategy
Vendor relationships
Margin optimization
Sales enablement
R&D initiatives
Emerging AI technologies
Internal prototypes
Production-ready services and IP
Generative AI and automation trends
AI solutions, automation workflows, and integrations
Prompting, model usage, and solution design best practices
Internal enablement assets
Playbooks and training
Cross-functional leadership
Profitability, revenue growth, and platform roadmap execution
Commercially viable, operationally scalable, and technically aligned solutions
High-performing AI practice team
AI initiatives with business goals and customer outcomes

Responsibilities

Define and execute the AI practice strategy, including offerings, target markets, and growth roadmap
Build and manage a portfolio of AI services and solutions (advisory, implementation, managed, resale)
Establish pricing, packaging, and commercial models across PS and MRR
Own practice-level performance including revenue growth, margin, and profitability
Lead customer workshops, discovery sessions, and advisory engagements
Translate business challenges into scalable AI-driven solutions
Act as a trusted advisor on AI adoption, use cases, and ROI realization
Support sales efforts through pre-sales solutioning and proposal development
Design and deliver AI implementation engagements (POCs, pilots, deployments)
Build repeatable delivery frameworks and methodologies
Develop on-demand service offerings that are easy to scope, sell, and deliver
Ensure delivery quality and measurable business outcomes
Develop and scale recurring AI service offerings, including AI optimization and tuning, automation lifecycle management, and Copilot adoption and enablement
Convert project-based work into long-term managed services
Drive AI as a core attach opportunity across the client base
Identify and onboard third-party AI platforms and products for resale
Define and execute a resale and partner strategy aligned to F12’s model
Package products with services to create bundled MRR offerings
Manage vendor relationships, pricing, and margin optimization
Enable sales on attach, upsell, and positioning strategies
Lead structured R&D initiatives to evaluate emerging AI technologies
Run experiments, pilots, and internal prototypes
Convert validated use cases into production-ready services and IP
Maintain a pipeline of innovation feeding new PS and MRR offerings
Stay ahead of trends in generative AI and automation
Guide development of AI solutions, automation workflows, and integrations
Establish best practices for prompting, model usage, and solution design
Build internal enablement assets including playbooks and training
Work collaboratively across CFO, CRO, and CTO portfolios to align AI strategy with profitability, revenue growth, and platform roadmap execution
Ensure solutions are commercially viable, operationally scalable, and technically aligned
Build and lead a high-performing AI practice team over time
Align AI initiatives with business goals and customer outcomes