Principal AI Engineer

Requirements

• 8+ Years of Engineering Excellence: You are a Staff-level engineer first. You’ve built systems that handle high scale, and you know how to architect for long-term maintainability and performance
• Agentic Curiosity: You’ve moved beyond the "chatbot" phase and are actively experimenting with AI Agents. You understand that the challenge isn't the prompt, but the orchestration, state management, and reliability of the agent's actions
• Systems Thinker (Non-Deterministic Mindset): You recognize that AI is probabilistic. You are excited by the challenge of building deterministic "wrappers" and Evaluation loops around models to make them safe for production
• The "Applied" Edge: You likely come from a background in distributed systems, internal platforms, or developer tooling, and you're now applying that rigor to the AI stack
• Beyond the Wrapper: You have serious experience moving beyond simple API calls to architecting multi-stage AI orchestrations (agents, chained workflows, or custom runtime logic)
• Orchestration Experience: Even if you aren't an AI researcher, you have experience building complex, multi-step workflows (e.g., temporal systems, state machines, or event-driven architectures) and want to apply this to Agentic loops
• Reliability Obsession: You understand why "vibes-based" testing doesn't work. You’ve started exploring or building Eval frameworks to measure how models perform against real-world data
• Infrastructure Mindset: You are comfortable with the "glue" that makes AI work: vector databases, semantic caching, and API integration with third-party tools
• Strong backend experience (Python preferred)
• Experience with distributed systems and event-driven architectures
• Familiarity with tools like Kafka, Pub/Sub, or equivalent
• Experience working with LLMs (prompting, RAG, agents, evaluation workflows)
• Experience building APIs and scalable services
• Understanding of monitoring, observability, and system performance

What the job involves

• This role will focus on building and scaling the systems that support AI evaluation and iteration, helping the team move faster and more reliably
• You’ll have a chance to:
• Work on production AI systems used by thousands of businesses
• Define how we evaluate and improve AI performance at scale
• Build internal platforms and tooling used by AI and engineering teams
• Reduce manual processes and improve iteration speed on AI features
• Collaborate across AI, ML, and product teams
• Raise the engineering bar and mentor others
• End-to-End Platform Ownership: Architect and lead the development of our internal evaluation platform, moving the needle from manual testing to a fully automated lifecycle (from LLM-as-a-judge creation to production monitoring)
• Accelerate Time-to-Market: Directly impact our primary KPI by designing tools and workflows that drastically reduce the time it takes to deliver a calibrated, production-ready agent
• Infrastructure Collaboration: Partner with the Orchestration team to build the robust, scalable infrastructure required to run complex evals and agentic simulations at scale
• Squad Empowerment: Serve as the "AI Technical Lead" for product squads, guiding them through the complexities of agent design, failure analysis, and prompting best practices
• Decentralize Quality: Instead of being a bottleneck, you will build the "paved road" that allows product squads to become autonomous in measuring and maintaining their own agent quality
• Standard Setting: Define what "good" looks like for AI at (Company Name). You’ll translate non-deterministic AI behavior into predictable engineering metrics that the whole organization can trust
• Mentor & Level Up: Bridge the gap between traditional software engineering and AI. You’ll mentor engineers on how to apply rigorous system design to the world of LLMs and agents
• Continuous Observability: Take ownership of the feedback loop, ensuring that production insights from our agents directly inform the next iteration of our evaluation datasets