Principal Software Engineer, Applied AI

• Design and build agentic AI systems that orchestrate multi-step workflows across heterogeneous enterprise content – documents, CRM data, analytics, and more – to deliver contextual, actionable answers for sales teams.

• Own the AI platform architecture – define the patterns, abstractions, and guardrails that enable multiple engineering teams to ship AI-powered features safely and independently.

• Build evaluation and observability infrastructure for non-deterministic systems –  including automated regression testing, LLM-as-judge pipelines, and production quality monitoring – so the team can ship AI features with confidence.

• Drive model strategy and cost efficiency – evaluate and route across model providers, optimize context windows, and make principled latency/quality/cost tradeoffs at enterprise scale.

• Raise the AI engineering bar across the organization – not just within your team, but across engineering. Coach engineers on applied AI best practices, build shared tooling, and cultivate a culture where AI fluency is widespread.

• Drive operational excellence – ensure enterprise-grade reliability, security, and performance for all AI-powered features in production.

• Communicate complex technical concepts clearly to both technical and non-technical audiences, and translate business needs into actionable engineering investments.

What will set you apart:

• Demonstrated depth in shipping production agentic AI systems – you've built multi-step, tool-using agents that operate on real data at scale.

• Deep expertise in context engineering: retrieval pipelines, ranking, prompt orchestration, and the tradeoffs involved in assembling context for LLMs over large, diverse document corpora.

• Hands-on experience building evaluation frameworks for non-deterministic AI systems – you understand why this is hard and have opinions on what works (synthetic evals, human-in-the-loop, LLM-as-judge, production monitoring).

• Track record of designing systems that balance quality, latency, and cost – including experience with model selection, routing, and optimization across providers.

• Strong instinct for platform thinking: you know how to build abstractions that accelerate other teams without over-constraining them.

Foundations:

• 8+ years of professional software engineering experience, with significant time spent on distributed, data-intensive production systems.

• Strong programming skills in Python, Java, TypeScript, or equivalent. You're comfortable across the stack.

• Experience developing and operating cloud services at enterprise scale (AWS, Azure, or GCP).

• A track record of full product lifecycle ownership – from technical design through iterative shipping to production operation.

• Experience mentoring engineers and building collaborative, high-performing teams.

• Strong cross-functional collaboration skills — you work effectively with ML researchers, data scientists, product managers, and business stakeholders with different backgrounds and priorities.