Practice Lead - Data & Ai

Job Title: Practice Lead – Data & AI
Company: Architecture in Motion (AIM)
Location: Greater Toronto Area, Canada
Employment Type: Full-time
Reporting To: VP, Microsoft Business Solutions
The OpportunityAIM is seeking a Data & AI Practice Lead to build and lead our Microsoft-focused Data & AI practice.
You will own the practice’s technical direction, delivery quality, team development, and commercial performance. The role requires deep Microsoft Fabric expertise, practical data science capabilities, and strong AI engineering knowledge, together with the ability to translate those capabilities into compelling client solutions.
This is a hands‑on leadership position. You must be comfortable engaging executives, shaping proposals, designing architectures, reviewing code, resolving technical challenges, and developing the team.
Key ResponsibilitiesPractice strategy and growth
Define and execute the Data & AI practice strategy, service portfolio, capability roadmap, and hiring plan.
Partner with business development and Microsoft alliance leadership to build qualified pipeline and win new customers.
Develop repeatable offerings across data modernization, Microsoft Fabric, predictive analytics, AI applications, and managed data services.
Own practice revenue, gross margin, utilization, delivery capacity, and forecasting against agreed targets.
Establish clear differentiation through industry expertise, reusable intellectual property, and demonstrable business outcomes.
Support Microsoft partner designations and advanced specializations through skilling, customer delivery evidence, and technical readiness.
Technical leadership and architecture
Serve as the senior technical authority for Data & AI engagements.
Lead solution architecture across Microsoft Fabric, Azure data services, data science, and AI engineering.
Establish standards for data architecture, integration, modelling, security, governance, testing, deployment, and operations.
Make sound build-versus-buy decisions and select technologies based on client requirements, economics, and operational needs.
Remain directly involved in critical designs, technical reviews, proofs of concept, and complex delivery issues.
Ensure solutions are secure, maintainable, scalable, and ready for production.
Microsoft Fabric and enterprise data
Lead implementations spanning OneLake, Lakehouse, Warehouse, data pipelines, notebooks, semantic models, and Power BI.
Guide integration with Dynamics 365 Business Central, Finance & Operations, Dataverse, and other enterprise systems.
Establish reliable ingestion, transformation, data quality, lineage, and monitoring practices.
Oversee performance tuning, capacity planning, and cost optimization.
Develop trusted data foundations that support reporting, predictive analytics, and AI applications.
Data science and AI engineering
Identify business problems suited to predictive analytics, machine learning, and generative AI.
Guide model development for forecasting, anomaly detection, customer segmentation, inventory optimization, and related use cases.
Challenge model assumptions, evaluation methods, and business value before production deployment.
Lead the engineering of enterprise AI applications, including retrieval‑augmented generation, search, agent workflows, and application integration.
Establish practices for model and AI evaluation, deployment, monitoring, access control, and responsible use.
Ensure the team can move solutions from demonstrations into reliable production services.
Client engagement and solution selling
Lead executive discovery sessions, technical workshops, assessments, and architecture discussions.
Translate business priorities into phased roadmaps with clear outcomes, scope, dependencies, and investment requirements.
Own the technical integrity of proposals, estimates, statements of work, and delivery commitments.
Present solutions credibly to business executives, IT leaders, data teams, and procurement stakeholders.
Identify opportunities to expand client relationships through measurable value and trusted advice.
Contribute to demonstrations, thought leadership, events, and Microsoft co‑sell activities.
Delivery and people leadership
Own delivery quality, technical risk, resource allocation, and escalation management across the practice.
Work with project management to maintain realistic plans, budgets, acceptance criteria, and change controls.
Recruit, mentor, and develop data engineers, analytics engineers, data scientists, and AI engineers.
Establish individual development and certification plans aligned with practice priorities.
Build a culture of engineering discipline, accountability, collaboration, and continuous learning.
Develop reusable accelerators and apply AI‑assisted delivery methods with appropriate quality controls.
Required Qualifications
Typically 10+ years in data, analytics, or related engineering roles, including experience leading technical teams and enterprise client engagements.
Demonstrated success building or leading a consulting practice, delivery team, or comparable Data & AI function.
Deep, hands‑on Microsoft Fabric expertise and experience delivering production solutions.
Strong technical foundation in SQL, Python, PySpark, data modelling, and modern data architectures.
Practical data science experience, including model development, validation, deployment, and monitoring.
Strong AI engineering experience involving LLMs, retrieval, embeddings, APIs, and production evaluation.
Experience designing secure enterprise solutions using Microsoft Azure and associated identity and governance services.
Proven ability to scope engagements, develop estimates, support sales, and manage delivery economics.
Experience managing people, developing capabilities, and holding teams accountable for outcomes.
Strong executive communication skills and the ability to connect technical decisions to business value.
Preferred Qualifications
Experience integrating Microsoft data and AI solutions with Dynamics 365.
Experience in distribution, supply chain, manufacturing, or other operationally complex industries.
Experience with Azure Machine Learning, MLflow, Azure AI Search, Microsoft Purview, and real‑time analytics.
Familiarity with Microsoft partner programs, co‑sell motions, solution designations, and specialization requirements.
Relevant Microsoft certifications across Fabric, Azure architecture, AI engineering, or data science. [DP-700, DP-600, AI-103, AI-300]
Experience developing packaged services, reusable accelerators, or managed service offerings.
What Success Looks LikeAreaExpected outcomePractice directionA clear service portfolio, capability roadmap, and execution plan aligned with AIM’s growth objectives.
Commercial performanceAchievement of agreed revenue, gross margin, pipeline contribution, and utilization targets.
Client deliveryPredictable execution, strong client satisfaction, and measurable business outcomes.
Technical capabilityProduction‑ready Fabric, predictive analytics, and AI solutions supported by consistent engineering standards.
Team developmentClear ownership, effective mentoring, targeted hiring, and progress against certification plans.
Microsoft alignmentDelivery evidence and technical readiness supporting AIM’s partner objectives.
First 90 Days
Assess the team, delivery portfolio, technical assets, and capability gaps.
Establish the practice operating plan, scorecard, service priorities, and resource requirements.
Take technical ownership of priority opportunities and active engagements.
Build an executive‑ready demonstration connecting Microsoft Fabric, predictive analytics, and AI to a relevant business scenario.
Implement baseline standards for architecture reviews, estimating, delivery quality, and production readiness.
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