Senior Principal Architect, AI-Native Platform Transformation

Medallia•Mclean, VA

8d•Hybrid

About The Position

Medallia is seeking a Senior Principal Architect to lead the architectural transformation of its platform from traditional enterprise software into an AI-native company. This role is crucial for establishing architectural patterns and practices that ensure the coherent development of AI capabilities across the platform. The architect will define the Enterprise AI Reference Architecture, delineate boundaries between centralized platform capabilities and product-owned innovation, and drive organizational convergence towards a unified platform vision over the next 3-5 years. The role requires anticipating future trends, particularly the increasing role of agents in platform interaction.

Requirements

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.
Demonstrated experience evolving legacy enterprise architectures toward modern, AI-centric or autonomous operational models.
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.
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.

Nice To Haves

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.

Responsibilities

Define the Enterprise AI Reference Architecture.
Establish standardized agent runtime patterns, including orchestration frameworks, agent lifecycle management, and execution environments.
Define the memory and context architecture for structuring, persisting, sharing, and scoping context 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, including provider-agnostic interfaces, routing and fallback strategies, and portability architecture.
Establish observability and evaluation standards for non-deterministic systems, including tracing, eval harnesses, quality gates, and cost telemetry.
Set AI Platform Strategy by defining the strategic boundary between centralized platform capabilities and product-owned innovation.
Lead build vs. buy decisions across the AI stack and formulate vendor abstraction strategies.
Own the platform consolidation roadmap, sequencing the convergence of AI implementations onto shared services.
Maintain the architectural decision record for the AI platform.
Drive Organizational Convergence by serving as the primary architectural liaison across various departments.
Run the architectural review and exception process for AI initiatives.
Identify and dismantle fragmented AI sprawl through standards, shared services, and influence.
Publish and evangelize reference implementations, golden paths, and architectural patterns.
Establish AI Governance & Operational Standards, including prompt and agent lifecycle standards, evaluation requirements, and AI incident management.
Architect frameworks for model auditability, agent permissioning and identity, and cost governance.
Define human oversight boundaries by autonomy class, with deterministic fallback strategies.
Partner with Security and Compliance to ensure the reference architecture satisfies enterprise, regulated, and government-cloud requirements.
Architect for the Agent-First Future by continuously testing the platform's readiness for a world where agents are primary actors.
Redefine platform contracts for agent consumption, including APIs, permission models, state management, and observability.
Anticipate second-order shifts in UX, workflows, and customer interaction models driven by agent-to-agent interactions.