Presales Solution Architect, Cloud, AI and Data
About Centrilogic
Centrilogic is a global provider of cloud, data, AI and managed services. We help organizations modernize their systems, adopt secure and scalable AI architectures, and run those platforms once they are live. As a Microsoft-focused managed services and cloud consulting partner, we work across the Microsoft stack: Azure infrastructure and networking, Microsoft 365 and Copilot, Microsoft Fabric, and enterprise-grade AI solutions.
Role overview
This is a senior, client-facing generalist role. You sit between Azure infrastructure, Microsoft AI, data platform modernization and the sales team, and you are the technical voice on pursuits that cross all three. In practice that means turning a client problem into a solution we can price, sell and deliver on Azure, Microsoft 365 Copilot, Azure AI Foundry and Microsoft Fabric.
You run presales engagements from the first discovery call through to the proposal, statement of work and hand off to delivery, working alongside Account Executives, SMEs and delivery teams. Designs have to be technically sound, and they have to answer the question the client actually asked.
We are looking for breadth first. You should be strong enough in Azure infrastructure and networking to hold a landing zone, migration or hybrid connectivity conversation, current enough in Microsoft AI to scope a Copilot or agentic AI engagement, and comfortable enough with Microsoft Fabric to qualify a data modernization opportunity and recognize when to pull in a specialist. Consultative presales experience matters more here than depth in any one product. You should also be a self-starter who is at ease walking into a client conversation without a script.
The first year
- First 90 days: you know our cloud, AI and data offerings well enough to lead discovery on a qualified opportunity without support.
- By six months: you own the technical narrative on your pursuits, and it shows in win rate and deal progression.
- Ongoing: proposals and SOWs that delivery can execute without re-scoping, plus reusable assets that make the practice more scalable and efficient.
Primary responsibilities:
Solution architecture
- Design cloud, AI and data architectures that solve a real client problem, using the Microsoft stack from Azure infrastructure and networking up through analytics and AI.
- Architect Azure infrastructure: landing zones, subscription and identity design, IaaS and PaaS migration patterns, resiliency and DR, and cost control.
- Design core Azure networking: hub-and-spoke and Virtual WAN topologies, ExpressRoute and site-to-site VPN, Azure Firewall, NSG and UDR segmentation, private endpoints and Private DNS.
- Design AI solutions on Azure AI Foundry, Azure OpenAI, Microsoft 365 Copilot and Copilot Studio, including agentic, RAG and multi-model patterns.
- Show clients how Microsoft Fabric and the wider Azure data estate underpin AI readiness using tools like; lakehouse and warehouse patterns, Power BI, and data governance in Microsoft Purview.
- Bring Power Platform (Power Apps, Power Automate, Power BI) into a design where the requirement is workflow automation or reporting.
- Apply responsible AI practices to every design: content safety, data governance, model transparency and compliance.
- Size and scope engagements with ROM estimates and a structured discovery approach.
- Be clear with clients about what capabilities are best implemented as a project and what belongs in ongoing managed services across infrastructure, security and AI operations.
Technical consultation
- Act as the senior technical advisor to sales leadership, account teams and client stakeholders through the whole sales cycle.
- Hold your own with C-level executives and with technical architects on cloud strategy, infrastructure and network modernization, AI adoption, security and data governance.
- Run discovery workshops and current-state assessments, and press on the problem before proposing a solution.
- Put a solution in business terms: the value narrative, the ROI model, the executive presentation.
- Stay current on the Microsoft stack, including Azure infrastructure and networking services, Azure AI Foundry, Microsoft 365 Copilot, Copilot Studio, Microsoft Fabric, and where Microsoft is heading with agents.
- Advise clients on AI governance and on what it takes to put an AI workload into production.
Sales support
- Work with Account Executives to build and advance opportunities and give them a technical read they can trust.
- Own the technical response in RFPs, competitive deals and executive briefings.
- Write the proposals, effort estimates, pricing models and SOWs, and see them through review.
- Build POCs and demos that show a client their own use case working.
- Make the business case for our cloud, AI, data and managed services capabilities.
- Use your network and industry profile to open doors and generate pipeline.
Working with our SMEs
- Work with SMEs across our practices (cloud, data, security, ERP and managed services) so the solution holds together end to end.
- Use delivery and implementation teams to validate a design before the proposal goes out and stay involved at kick-off.
- Peer review other proposals for accuracy, scope integrity and fit with how we deliver.
- Improve the practice through reusable assets, internal enablement and IP we can use on the next deal.
Qualifications:
Education
- University degree in computer science, engineering, information technology or an equivalent technical discipline.
Experience
- 7+ years in cloud architecture, infrastructure engineering, cloud solutions engineering or technical consulting on the Microsoft stack.
- 3+ years in presales, solution architecture or another customer-facing advisory role.
- Hands-on Azure infrastructure work: landing zones, server and workload migrations, hybrid connectivity and core Azure networking.
- AI solutions you have delivered in an enterprise setting: Microsoft 365 Copilot rollouts, agents built in Copilot Studio or Azure AI Foundry, and RAG patterns.
- Enough Microsoft Fabric or Azure data platform exposure to qualify a data modernization or AI-readiness opportunity and scope discovery with a data specialist.
- A record of running discovery workshops, writing proposals, sizing engagements and presenting to executives.
- Deals won and advanced in a consultative technical presales role.
Certifications
Required, or achieved within six months of hire:
- Microsoft Certified: Azure Solutions Architect Expert (AZ-305)
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
Preferred / Nice to Have:
- Microsoft Certified: Azure Network Engineer Associate (AZ-700)
- Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)
- Microsoft Certified: Agentic AI Business Solutions Architect
Core skills:
Azure infrastructure and networking
- Azure landing zones: management group and subscription design, Azure Policy, RBAC and governance at scale.
- Compute, storage and platform services: virtual machines, Azure VMware Solution, App Services, containers (Container Apps, AKS), backup and disaster recovery design.
- Core networking: hub-and-spoke and Virtual WAN, ExpressRoute and VPN, Azure Firewall, NSGs and UDRs, private endpoints, Private DNS, Application Gateway and Front Door.
- Migration: Azure Migrate assessments, wave planning, and Azure Arc for hybrid and multicloud estates.
- Identity and security: Entra ID, Conditional Access, Zero Trust principles and Defender for Cloud posture management.
- Commercials: Azure pricing and sizing, reservations and savings plans, CSP and EA constructs, and FinOps basics.
AI platform, Microsoft first
- Azure OpenAI Service: model selection, deployment, prompt engineering, fine-tuning and responsible use.
- Azure AI Foundry: orchestration, evaluation, the model hub and multi-model pipelines.
- Microsoft 365 Copilot: licensing, readiness assessment, the data governance work that must happen first, rollout and adoption, and extensibility across Teams, SharePoint and Outlook.
- Copilot Studio: enterprise agents, custom connectors and plugin architecture.
- Microsoft Agent Framework and Semantic Kernel: agentic workflow design, tool use and multi-agent orchestration.
- RAG architectures: vector stores (Azure AI Search, Cosmos DB), embeddings and chunking strategies.
- Model Context Protocol (MCP) and agent-to-agent (A2A) interoperability.
- Responsible AI: content safety, data governance, fairness, model transparency and regulatory compliance.
Data and analytics platform
- Microsoft Fabric: OneLake, lakehouse and warehouse patterns, Data Factory pipelines and Power BI semantic models, at a level that lets you qualify and scope an opportunity.
- Azure data services: Azure SQL, Cosmos DB, Azure AI Search, and Synapse to Fabric migration paths.
- Governance and AI readiness: Microsoft Purview, lineage, classification and sensitivity labelling, which Copilot and agent work depends on.
- Integration: REST APIs, event-driven messaging (Azure Service Bus, Event Grid), Logic Apps and hybrid connectivity.
- Power Platform: Power Apps, Power Automate and Power BI.
Presales and client engagement
- Solution selling: putting technical capability into commercial terms.
- Proposal writing, SOW development, effort estimation and pricing models.
- POC and demo design, including building or scripting one yourself.
- Strong written and spoken communication with executives and architects alike.
- Willingness to travel within the Greater Toronto Area, elsewhere in Canada, and to the United States as needed.
Nice to have
Experience with any of the following is an asset:
More Microsoft depth
- Deeper Fabric or Synapse implementation work: capacity sizing, medallion architecture and real-time intelligence.
- Microsoft Sentinel and Defender for Cloud, and how to design securely for AI workloads.
- Microsoft industry clouds for financial services, healthcare or manufacturing.
AI outside the Microsoft stack
- Anthropic Claude in an enterprise setting: Claude for Work, Cowork, Claude Code, agent skills, and deployment architecture.
- Choosing between models, and cross-vendor MCP integration where a non-Microsoft model is the better fit.
Application development and automation
- C#/.NET or Python on Azure PaaS or containers, enough to scope and estimate. You will not be asked to build it.
- DevOps and CI/CD: Azure DevOps, GitHub Actions, Bicep or Terraform.
Managed services and operations
- LLMOps and MLOps: model lifecycle, drift detection, evaluation frameworks and observability.
- FinOps for AI: token usage, cost modelling and consumption forecasting.
- Managed services design: SLAs, operational readiness, and ongoing platform and model governance.
Enterprise applications
- CRM: Dynamics 365 (Sales, Customer Service), Salesforce.
- ERP: Dynamics 365 Finance & Supply Chain, JDE, NetSuite.
