Principal Technical Consultant - AI/ Azure Cloud Platform Engineering

AHEAD helps large enterprises assess, design, build, and migrate to the Microsoft Azure platform, and increasingly helps those same enterprises design, build, and run AI agent platforms on top of it. This role sits in the AIC Platform Engineering practice, alongside consultants who lead Azure assessments, landing zone build-outs, and cloud migrations, and specializes that practice's Azure depth toward AI agent platforms.

As a Principal Technical Consultant, you own that work end to end at client sites: you assess current state, design the target platform, build it, wire up the delivery pipeline, and leave the client team able to run it without you.

This is a hands-on role with delivery leadership attached. You will write infrastructure code, agent code, and the documents that client governance boards approve, and you will lead the junior and mid-level consultants delivering alongside you. You will present your own work to engineering leaders and executives, and you will teach client makers, developers, and platform teams how to build on what you delivered.

Our clients include firms in regulated and confidentiality-driven industries, so the platforms you build are private-networked, policy-governed, and evaluated before anything reaches production.

What you will do: Consulting and delivery leadership

• Work directly with client stakeholders to interpret business and operational challenges and translate them into the right technical solution, whether that is an Azure landing zone, a migration wave, or an agentic platform: which platform fits, what data and governance boundaries apply, and what the first production candidate should be.
• Lead junior and mid-level consultants on your engagements: set technical direction, review their work, unblock them daily, and develop them into independent delivery owners.
• Shape engagements alongside AHEAD account teams: scope workstreams, define deliverables, estimate effort, and keep delivery on plan as client priorities shift.
• Build and maintain strong client relationships beyond the current statement of work, and identify opportunities to expand AHEAD's footprint through upsell and cross-sell.
• Serve as the senior technical voice in client steering and working sessions, presenting options with tradeoffs and a clear recommendation rather than a menu.
• Azure platform engineering
• Lead Azure discovery and assessment engagements: current-state architecture reviews, workload and application inventories, dependency mapping, and cloud readiness assessments that inform landing zone and migration roadmaps.
• Design and deploy enterprise-scale Azure landing zones aligned to the Cloud Adoption Framework: management group and subscription hierarchies, hub-spoke or Virtual WAN networking, policy-as-code guardrails, and identity and RBAC foundations.
• Plan and execute migrations to Azure: workload assessment and right-sizing with Azure Migrate, migration wave sequencing, cutover runbooks, and post-migration validation across compute, storage, databases, and containers.
• Establish hybrid and cross-premises connectivity: ExpressRoute, VPN gateways, and DNS strategies that connect client on-premises environments to Azure.
• Own cost governance and FinOps practices: budgets and alerts, tagging standards, reserved instance and savings plan strategy, and showback or chargeback reporting.
• AI agent platform engineering and delivery
• Design and deploy Microsoft Foundry environments for enterprise clients, including network-isolated configurations behind client firewalls: private endpoints, private DNS, subnet delegation, managed identity and RBAC design, and customer-managed keys.
• Stand up Copilot Studio governance for client tenants: environment strategy and environment groups, DLP baselines and connector governance, generative AI settings, channel approval flows, and licensing and capacity guidance.
• Build promotion pipelines in GitHub Actions that move agents from sandbox to production through automated tests, evaluation thresholds, and human approval gates implemented as environment protection rules.
• Implement evaluation and safety frameworks: golden datasets, quality, retrieval, and safety evaluators run in CI, and compensating controls for model paths where platform content filtering does not apply.