AI Principal Architect

Anaplan•Miami, FL

About The Position

This role sits within Anaplan's IT Customer Experience organization and is focused on transforming how Anaplan employees work. You will design, build, and scale AI tools, agents, and capabilities that drive productivity, smarter decision-making, and operational efficiency across our global workforce. Anaplan is building its Enterprise AI Center of Excellence (COE) — a one-stop shop for all internal AI at Anaplan — and is looking for an AI Principal Architect to lead it from a technical standpoint. This is a senior, newly created role at the intersection of hands-on enterprise AI engineering, internal system architecture, technical leadership, and workforce enablement. The AI Principal Architect will own the technical vision for Anaplan's internal AI — designing the reference architecture for LLMs, agentic AI, and Gemini Enterprise; setting engineering standards; evaluating emerging platforms; and ensuring every employee-facing initiative is built with rigor, scalability, and responsible design. This role carries a hybrid profile: deep individual technical contribution combined with team leadership influence. You will mentor engineers, guide architectural decisions, serve as the primary technical escalation point, and contribute a forward-looking perspective to the 2–3 year AI technology roadmap — without necessarily holding formal management authority over all contributors. You will report directly to the Director, IT Customer Experience, who co-leads the Enterprise AI COE alongside the Financial Flexibility Lead and a GTM Champion as a 3-in-a-box. You will collaborate closely with Technical/Engineering teams (dotted-line, for agent development and delivery) and the Transformation & Financial Flexibility team (to identify AI-driven cost-saving opportunities).

Requirements

7+ years of software or AI/ML engineering experience, with at least 3 years in a technical lead, staff engineer, or principal-level role — ideally with exposure to enterprise IT or internal tooling environments.
Deep hands-on expertise across the AI/ML stack: LLMs, retrieval-augmented generation (RAG), agent frameworks, or MLOps pipelines — with experience in enterprise or workforce deployment contexts.
Proven experience architecting and deploying enterprise-scale internal AI systems from design through production, including reliability, observability, and performance engineering.
Proficiency in Python and enterprise cloud AI platforms (GCP Vertex AI, Azure AI, or AWS SageMaker) and AI orchestration tooling.
Hands-on experience with Google Workspace AI and Gemini Enterprise, or equivalent enterprise AI platforms (e.g., Microsoft Copilot, OpenAI Enterprise).
Demonstrated ability to provide technical leadership, mentorship, and architectural guidance to engineering teams without formal management authority.
Ability to manage competing priorities, dependencies, and delivery timelines across multiple workstreams.
Strong business acumen and communication skills — able to translate complex enterprise AI/ML concepts into business value narratives for C-level audiences.
Experience in enterprise SaaS environments with complex stakeholder landscapes, security requirements, and compliance obligations.

Nice To Haves

Experience with AI governance frameworks, responsible AI principles, or enterprise AI policy and risk management for internal deployments.
Familiarity with ServiceNow or similar ITSM platforms and integration patterns.
Background in enterprise change management, internal AI enablement, or large-scale digital transformation programs.
Experience working with cross-functional finance or business transformation teams on AI-driven cost optimization.
Prior experience in an internal IT or enterprise technology function at a high-growth SaaS company.
Enterprise technology function at a high-growth SaaS company.

Responsibilities

Own and publish the reference architecture for Anaplan's internal AI — covering LLMs, agentic AI frameworks, Gemini Enterprise integrations, and data pipelines.
Architect end-to-end internal enterprise AI solutions designed for the scale, security, and reliability requirements of a global workforce.
Set and enforce AI engineering standards, coding practices, code review processes, and technical guardrails across all internal AI projects.
Lead hands-on technical design reviews and guide implementation decisions from intake through production deployment.
Evaluate, select, and onboard new enterprise AI platforms and tools — providing technical recommendations grounded in internal workforce needs.
Design the LLM abstraction layer and multi-model strategy to optimize cost, performance, and flexibility.
Own technical debt management, system observability, and scalability planning for Anaplan's internal AI systems.
Define and maintain technical evaluation criteria for the AI intake process — assessing feasibility, build complexity, security risk, and integration requirements for employee-facing tools.
Ensure all internal AI initiatives pass engineering, security, compliance, and responsible AI review before development begins.
Maintain enterprise AI technical policies, engineering standards, and responsible AI guardrails in partnership with Security and Compliance.
Translate approved internal use cases into clear technical specifications and hand off to engineering teams with full architectural context.
Define enterprise AI data layer and integration architecture across core systems (Salesforce, ServiceNow, Gainsight, Workday).
Integrate with ServiceNow AI Control Tower to track all AI use cases, agents, and realized business value.
Implement human-in-the-loop, explainability, and auditability frameworks for all decision-impacting AI systems.
Partner with Finance to define and measure AI-driven ROI, ensuring alignment with enterprise value targets.
Establish full lifecycle management for AI agents, including monitoring, feedback loops, and continuous improvement.
Provide technical direction, coaching, and mentorship to engineers across the AI Governance & Enablement and AI Operations & Insights teams.
Serve as the primary technical escalation point for complex internal AI delivery challenges and architecture decisions.
Partner with Technical/Engineering teams (dotted-line) on agent architecture, build quality, and delivery timelines.
Help define the technical bar for AI team hiring and contribute to onboarding and skills development programs.
Foster a culture of engineering rigor, experimentation, and continuous improvement across the internal AI function.
Oversee the technical deployment and operationalization of AI agents and solutions, ensuring production readiness and stability for Anaplan employees.
Drive Gemini Enterprise adoption and lead AI Friday sessions with genuine technical depth.
Build and maintain engineering dashboards tracking internal AI system performance, adoption rates, and ROI metrics.
Translate engineering decisions into executive-ready business value narratives for the Director, CIO, and Finance audiences.
Research and prototype emerging AI/ML techniques, large language models, agent frameworks, and enterprise tooling to identify internal productivity and cost-saving opportunities.
Design and lead proof-of-concept pilots that validate novel internal use cases before committing to full engineering investment.
Build and maintain the AI innovation pipeline — a living backlog of high-potential experiments.
Contribute the technical perspective on where internal AI capabilities should be invested over the next 2–3 years, collaborating with the Director on the roadmap.
Work hand in hand with the Transformation & Financial Flexibility team to identify and technically validate AI-driven cost-saving opportunities in Anaplan's internal operations.
Act as technical advisor and AI subject matter expert to business units identifying internal AI use cases.
Support the 3-in-a-box COE co-leadership (IT, Finance, GTM) with technical analysis, architecture reviews, and feasibility assessments.
Build trusted technical relationships across Engineering, Security, Compliance, Legal, and HR.