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Architecture Full-time · Remote / Hybrid

AI Architect

Design practical, scalable AI systems that move from experimentation to trusted production use. Shape architecture across ML, generative AI, retrieval, governance, and operational readiness.

Role Summary

We are seeking an AI Architect to help organizations design practical, scalable AI systems that move from experimentation to trusted production use. This role defines the architecture behind applied machine learning, generative AI, retrieval systems, decision intelligence, and operational AI products. The right candidate understands not only model architectures, but the surrounding system design required for success: data readiness, evaluation, safety, governance, integration, observability, and operating model.

What You'll Do

Core Responsibilities

  • Design architectures for applied AI solutions including ML systems, LLM-based applications, RAG workflows, and decision-support products.
  • Define reference architectures for model development, deployment, monitoring, evaluation, and lifecycle management.
  • Guide teams on AI system design patterns including prompt orchestration, retrieval, agent workflows, feature pipelines, and inference services.
  • Align AI architecture with enterprise data, security, and platform standards.
  • Review solution designs to ensure reliability, traceability, and production readiness.

Strategic & Cross-Functional Responsibilities

  • Translate business opportunities into AI solution approaches with clear technical and operational implications.
  • Advise on model selection, build-vs-buy decisions, hosted vs self-managed patterns, and risk management.
  • Partner with stakeholders to establish governance guardrails for responsible and effective AI use.
  • Shape roadmaps for AI capabilities that can scale across domains and products.
  • Contribute to reusable AI patterns, frameworks, and accelerators.

What You Bring

Required Qualifications

  • 8+ years in architecture, ML, AI engineering, or software/data platform roles with deep experience in AI solution design.
  • Experience designing end-to-end ML or generative AI systems in production environments.
  • Strong understanding of model lifecycle, evaluation, deployment, monitoring, and cost/performance tradeoffs.
  • Experience with enterprise integration, security, and data platform considerations.
  • Ability to communicate clearly with executives, product leaders, engineers, and risk stakeholders.

Preferred Qualifications

  • Experience with RAG, knowledge layer design, semantic retrieval, or agentic workflows.
  • Familiarity with AI governance, model risk, privacy, and regulated environments.
  • Experience with MLOps or LLMOps frameworks and platform design.
  • Consulting or client-facing experience.
  • Experience leading architecture decisions across cross-functional teams.

Skills and Capabilities

Technical Skills

AI solution architecture (ML, NLP, LLM, retrieval)Evaluation design and release gatesInference architecture and latency-aware designModel lifecycle (registries, monitoring, rollback, governance)Secure integration patterns for enterprise AI

Domain & Business Skills

Mapping AI solutions to real decision workflowsDefining pragmatic thin slicesBalancing innovation with reliability and complianceStructuring AI systems as repeatable productsHelping leaders understand capability maturity

Tools, Platforms, and Languages

Python, SQL, APIs, notebooksCloud AI and data platformsVector databases and retrieval toolingModel registries, experiment tracking, evaluation harnessesContainerization and deployment tooling

What Success Looks Like

  • AI systems are architected for adoption, not just demos.
  • Teams move from prototype to production with less rework.
  • Solution designs are reliable, explainable, and maintainable.
  • Governance and operational readiness are built in from the start.
  • AI capabilities become reusable across multiple products or domains.

How You'll Collaborate

Internal Partners

AI Engineers, Data Scientists, Data Architects, Product Managers, Strategists

Client Partners

Technology leaders, product owners, risk/compliance stakeholders, business sponsors

We are committed to creating an inclusive workplace and providing equal opportunity to all applicants and employees. We welcome candidates from all backgrounds and provide reasonable accommodations throughout the hiring process.