Principal Architect, AI-Native Platform Transformation (McLean)

Overview

Medallia is the pioneer and market leader in Experience Management. Our award‑winning SaaS platform, Medallia Experience Cloud, leads the market in the management of experiences, insights, and actions for candidates, customers, employees, patients, and residents alike.

We believe that every experience is a memory that can last a lifetime. Experiences shape the way people feel about a company and greatly influence how likely people are to advocate, contribute, and stay. At Medallia, we are committed to creating a world where organizations are loved by their customers and their employees.

We empower exceptional people to create extraordinary experiences together.

Bring your whole self.

At Medallia, we believe every experience can inspire loyalty, trust, and growth. Our platform helps the world’s leading brands capture signals across customer and employee journeys and transform them into real‑time action using AI and analytics at enterprise scale. We are building the next generation of AI‑native platform capabilities that power intelligent automation, orchestration, and decision‑making across our products and we need an architect to ensure that evolution happens coherently.

Mission

As the Principal Architect, you will own the end‑to‑end architectural transformation of Medallia from traditional enterprise software into an AI‑native platform company. AI technology and innovation are advancing at a remarkable pace, and we are building AI capabilities across every part of our platform. Your mission is to establish the architectural patterns and practices that ensure this work compounds toward a platform vision rather than fragmented features. Define the Enterprise AI Reference Architecture, draw the boundaries between centralized platform capability and product‑owned innovation, and drive the organizational convergence that makes the platform evolve as one system over the next 3‑5 years aligned to emerging standards for AI systems and agent protocols. You are the architect who sees around corners and where the world is going, anticipating what the platform must become when agents, not humans, are the primary actors.

Responsibilities

Define the Enterprise AI Reference Architecture

• Establish standardized agent runtime patterns: orchestration frameworks, agent lifecycle management, and execution environments that product teams build on rather than reinvent.
• Define the memory and context architecture: how customer, session, and domain context is structured, persisted, shared, and scoped across agents and products.
• Set standards for agent communication and interoperability (MCP, A2A, and emerging protocols), eventing, and multi‑agent coordination patterns.
• Design the model abstraction layer: provider‑agnostic interfaces, routing and fallback strategies, and the portability architecture that preserves negotiating leverage and cost flexibility.
• Establish observability and evaluation standards for non‑deterministic systems: tracing, eval harnesses, quality gates, and cost telemetry as first‑class architectural concerns.

Set AI Platform Strategy

• Draw and defend the strategic boundary between centralized platform capabilities (runtimes, model gateway, context services, eval infrastructure) and what remains product‑owned, with clear interface contracts between the two.
• Lead build‑vs‑buy decisions across the AI stack and formulate the vendor abstraction strategy that prevents lock‑in at the model, framework, and infrastructure layers.
• Own the platform consolidation roadmap: sequence the convergence of overlapping AI implementations onto shared services without freezing product velocity.
• Maintain the architectural decision record for the AI platform, making trade‑offs explicit, durable, and revisitable as the landscape shifts.

Drive Organizational Convergence

• Serve as the primary architectural liaison across Product, Engineering, Data, Security, Infrastructure, Applied AI, and Enterprise Architecture.
• Run the architectural review and exception process for AI initiatives: a lightweight governance mechanism that catches divergence early without becoming a bottleneck.
• Identify and dismantle fragmented AI sprawl, including duplicative agent frameworks, redundant model integrations, and inconsistent context handling through standards, shared services, and influence rather than mandate alone.
• Publish and evangelize reference implementations, golden paths, and architectural patterns that make the standard path the easiest path.

Establish AI Governance & Operational Standards

• Define operational governance for AI systems: prompt and agent lifecycle standards (versioning, review, rollout, rollback), evaluation requirements, and AI incident management.
• Architect the frameworks for model auditability, agent permissioning and identity, and cost governance, making autonomy safe and accountable at enterprise scale.
• Define human oversight boundaries by autonomy class, with deterministic fallback strategies for every autonomous pathway.
• Partner with Security and Compliance to ensure the reference architecture satisfies enterprise, regulated, and government‑cloud requirements by design rather than exception.

Architect for the Agent‑First Future

• Continuously pressure‑test the platform with the question: what architecture survives when agents become the primary actors instead of humans?
• Redefine the platform’s contracts for that world, with APIs designed for agent consumption, permission models for non‑human identity, state management for long‑running autonomous workflows, and observability that explains agent behavior to humans.
• Anticipate the second‑order shifts: how UX, workflows, and customer interaction models change when customers’ agents interact with Medallia’s agents, and ensure the architecture is ready before the need is urgent.

Candidates based in the Tysons vicinity will be prioritized as this role is Hybrid, 3 days per week onsite.

Qualifications

Minimum Qualifications

• 10+ years of software engineering experience designing and operating large‑scale distributed systems and platforms, with deep expertise in backend systems, cloud‑native infrastructure, and platform engineering.
• Demonstrated, hands‑on experience building AI/ML infrastructure, agent orchestration systems, or developer platforms in production. You have built these systems, not just evaluated or consumed them.
• Demonstrated experience evolving legacy enterprise architectures toward modern, AI‑centric or autonomous operational models, including the migration strategy and sequencing, not just the target state.
• Demonstrated working fluency with the modern agentic stack: LLM serving and routing, agent frameworks and SDKs, tool‑integration protocols (MCP or comparable), evaluation infrastructure, and context/memory architectures.
• Demonstrated ability to lead complex cross‑functional technical initiatives and to drive adoption of architectural standards through influence, clarity, and credibility rather than authority alone.
• Demonstrated experience authoring Architecture Decision Records (ADRs), reference architectures, and executive narratives for systems impacting engineering teams, with a demonstrated ability to present technical trade‑offs to VP‑level or C‑level stakeholders.

Preferred Qualifications

• Deep expertise operationalizing LLMs, multi‑agent frameworks, and autonomous workflow paradigms at enterprise scale (LLMOps/AgentOps).
• Knowledge of AI safety, policy enforcement, and responsible AI operational practices, particularly in compliance‑sensitive or regulated environments.
• Experience with multi‑tenant SaaS platform architecture and the particular challenges of per‑customer configuration, data isolation, and schema variability.
• Track record establishing architecture governance functions (review boards, ADR practices, golden paths) that teams experience as enabling rather than obstructing.

What Success Looks Like

• A unified Enterprise AI Reference Architecture exists, is versioned and maintained, and is the default starting point for every new AI initiative, measured by adoption across product and engineering teams in shipped platform projects.
• New AI capabilities ship on shared platform services (runtime, model gateway, context, evals) rather than bespoke stacks; duplicative implementations are measurably consolidated over time.
• Build‑vs‑buy and vendor abstraction decisions are made deliberately and documented ensuring the company can switch model providers or frameworks without re‑architecture.
• AI governance is operational, not aspirational: every production agent has defined permissions, evaluation gates, audit trails, cost accountability, and a deterministic fallback.
• The platform is demonstrably ready for the agent‑first paradigm ahead of demand, with agent‑consumable APIs, non‑human identity, and autonomous workflow support exist before product teams are blocked waiting for them.

Why Join Medallia

• Define the foundational AI platform strategy of an industry‑leading enterprise SaaS company at the moment the architecture is being decided.
• Work on deeply technical, high‑impact platform challenges at massive scale with millions of users, global brands, enterprise and regulated environments.
• Influence the future of AI‑native product development across the entire organization; your reference architecture becomes how the company builds.
• Collaborate with exceptional en]]>