AI Architect

PairSoft

2d•Remote

About The Position

This is a foundational hire. AI is being built and shipped across every product line in the portfolio, often in isolation. Duplication of models, tools, and infrastructure is driving up cost and slowing us down. The AI Architect will lead the definition of our target-state AI architecture, stand up the central AI services platform that every product will consume, and drive the delivery roadmap that consolidates and scales AI across the portfolio. The role is technical and strategic. You will make the architecture decisions, then own their execution. You will build a small central team over time, but in the first six months you will operate primarily as a hands-on architect and delivery lead. AI is Phase 1 of a broader central services strategy at PairSoft; the pattern you set here will extend to other shared capabilities.

Requirements

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 Haves

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.

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.