AI Architect

Quick Facts

• Role: AI Architect

Description

You will set the technical direction for how we design, evaluate, integrate, and operate AI systems that improve important human workflows. This is a strategic, hands-on role focused on reference architectures, evaluation discipline, production readiness, and prototyping/validating key AI patterns (from deterministic workflows to retrieval-augmented systems and agents).

Responsibilities

• Define AI architecture direction (reference architectures and design principles) across AI services, agents, model integrations, data/context flows, tools, and human review

• Build evaluation practices (datasets, evaluation methods, regression checks, and quality thresholds) and use results to prioritize improvements

• Drive production readiness practices, including tracing/observability, versioning, monitoring, incident response, privacy/security, and lifecycle management

• Enable teams to ship reusable patterns and reference implementations; coach engineers through architecture reviews

• Connect AI architecture to outcomes by partnering with product and operations to measure workflow improvements

• Stay close to implementation by prototyping high-risk/foundational designs and reviewing critical implementations

• Track and recommend adoption of models, agent runtimes, orchestration approaches, and evaluation tooling based on evidence

Requirements

• 8+ years backend software development

• 3+ years AI architecture with demonstrated organization-wide impact

• Production software delivery experience with substantial AI/ML-powered systems work

• Track record of setting technical direction across teams using evidence and collaboration

• Practical LLM application understanding: retrieval, context construction, tool use, agent patterns, model selection, failure handling

• Ability to define AI quality expectations and build evaluations tied to production outcomes

• Good judgment on appropriate use of AI vs deterministic approaches

• Experience with production concerns: observability, privacy/security, reliability, cost, latency, and safe human escalation

• Strong communication across engineering, product, and executive audiences; builder mindset with comfort prototyping and review