Overview

Job Title: AI Architect

Responsibilities

• Define and publish the target-state AI architecture across the product lines, including central services versus application-layer boundaries
• Own the technology decisions: foundation model providers, orchestration frameworks, vector stores, evaluation and observability platforms, guardrails, and agent frameworks
• Establish architecture decision criteria and the decision-record process. Ensure every significant call is defensible, documented, and revisitable
• Design for tenant isolation, PII handling, and regional data residency from day one, working with the CISO on the security envelope
• Lead the build of the central AI platform: model gateway, prompt registry, RAG-as-a-service, guardrails and content safety, evals and telemetry, cost attribution, and audit logging
• Own the phased roadmap: discovery and decisions, platform build, migration of existing product AI features to the platform, and optimization
• Partner with product engineering leaders across all product lines to migrate existing AI features onto the central platform without disrupting customer commitments
• Set the operational bar: observability, SLOs, incident response, and on-call model for AI services
• Stand up per-request cost tagging and the AI unit economics dashboard. Track cost per feature, per customer, and per model. Enforce budgets and rate limits per tenant
• Chair a cross-product AI Council to govern shared standards, new use case intake, and vendor decisions
• Own vendor relationships for AI infrastructure and tooling. Lead build-versus-buy analysis and commercial negotiation with product and finance partners
• Prepare and represent the AI architecture and roadmap in leadership reviews and private-equity sponsor conversations, including technical due diligence
• Recruit and lead the AI Platform team as the function grows
• Establish the operating model between the central platform team and embedded AI engineers across product lines
• Mentor engineers across the portfolio on AI patterns, evaluation practices, and safe deployment

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or equivalent
• 10+ years of engineering experience, with at least 5 years in senior architecture roles at enterprise B2B SaaS companies
• 3+ years of hands-on production experience with LLM-based systems: RAG, agents, evaluation, prompt engineering, and inference optimization
• 3+ years people management experience, including recruitment, motivation and development of 3+ teams in global setting
• Demonstrated track record building an AI or ML platform consumed by multiple product teams. You have designed and shipped shared infrastructure, not just individual features
• Strong delivery leadership. You have shipped platforms on time, on budget, and with the operational discipline required to run them in production
• Deep cloud-native architecture experience on Azure and AWS. Comfort with Kubernetes, IaC, and modern data platforms
• Fluency across the modern LLM ecosystem: OpenAI, Anthropic, Azure OpenAI, Bedrock, open-source models, LangChain or LlamaIndex, vector databases, and observability tools like LangSmith or Langfuse
• Clear grasp of AI security and compliance: prompt injection, tenant isolation, PII handling, SOC 2, GDPR, and the vendor risk questions PE-owned SaaS companies face
• Ability to communicate architecture decisions and tradeoffs to executive audiences, including private equity sponsors and technical due diligence teams
• Fluency in English

Nice to have

• Domain experience in procure-to-pay, ERP integration, accounts payable, procurement, or adjacent finance and operations software
• Prior experience at an international PE-backed B2B SaaS company, ideally one that went through or is preparing for a liquidity event
• Experience integrating and consolidating AI capabilities across products acquired through mergers and acquisitions
• Direct experience with agent frameworks in production (LangGraph, AutoGen, or custom orchestration), fine-tuning, and multi-model routing
• Experience leading a central platform team through a technology consolidation program
• Prior participation in technical due diligence, either as owner or advisor