AI Architect

About the Role

We are building AI capabilities that improve important human workflows across the company. As AI Architect, you will set the technical direction for how we design, evaluate, integrate, and operate AI systems. You will help teams choose the right approach for each problem—from conventional software and deterministic workflows to retrieval-augmented systems and agents—and ensure those systems meet clear standards for quality, reliability, safety, and value.

This is a strategic, hands‑on role. You will establish reusable architecture and evaluation practices, guide cross-functional teams, and prototype or validate key patterns where needed. You will partner with product and engineering leaders to turn high‑value opportunities into systems that can be measured and operated in production.

• Set AI architecture direction: define reference architectures and design principles for AI services, agents, model integrations, data and context flows, tools, and human review. Make trade‑offs among quality, autonomy, reliability, latency, cost, and complexity.
• Build the evaluation discipline: help teams define expected behavior and success measures; design representative datasets and evaluation methods; write and maintain evals; establish regression checks and quality thresholds; and use results to prioritize improvements.
• Guide production readiness: shape shared practices for tracing and observability, versioning, monitoring, incident response, privacy, security, and lifecycle management of AI systems.
• Enable teams to ship: create reusable patterns, reference implementations, and practical guidance. Coach engineers through architecture reviews and help teams move valuable work from prototype to production.
• Connect architecture to outcomes: partner with product, operations, and domain teams to identify workflow needs and measure whether AI improves completion, quality, time, or other agreed outcomes.
• Stay close to implementation: prototype high‑risk or foundational designs, review critical implementations, and use production evidence to refine architecture.
• Track the evolving ecosystem: evaluate models, agent runtimes, orchestration approaches, and evaluation tooling against our requirements, and recommend adoption based on evidence.

• At least 8 years of experience in backend software development.
• At least 3 years of experience in AI architecture with demonstrated organization‑wide impact.
• Strong experience designing and delivering production software systems, with substantial work on AI/ML‑powered products or services.
• A track record of setting technical direction across teams and influencing decisions through evidence and collaboration.
• Practical understanding of LLM applications, including retrieval and context construction, tool use, agent patterns, model selection, and failure handling.
• Experience defining quality expectations and building evaluations for AI behavior; able to connect offline evaluations to production outcomes.
• Sound judgment about where AI is appropriate and when a simpler deterministic approach is better.
• Experience with production concerns such as observability, privacy and security, reliability, cost, latency, and safe human escalation.
• Clear communication across engineering, product, and executive audiences, with a builder's mindset and comfort prototyping and reviewing implementation.

• Establishing shared AI platforms, service patterns, or architecture standards across multiple teams.
• Creating evaluation datasets, test harnesses, regression suites, or quality dashboards for LLM or agent systems.
• Operating AI products with measurable user or business outcomes.
• Working with cloud AI platforms, model APIs, agent frameworks,…