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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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this 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...
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