Staff AI Engineer

This is a heavy-hitting Staff AI Engineer role meant for someone who loves writing code just as much as they love designing production-grade AI systems. You'll be splitting your time between hands-on development and acting as the technical compass for an internal AI team building enterprise-grade tools.

What You'll Actually Be Doing

Expect to take messy, ambiguous business problems and turn them into robust production systems using LLMs, agentic workflows, and RAG architectures. Your day-to-day will involve hands-on coding in Python, making tough architectural trade-offs around cost and latency, and mentoring other engineers to level up their AI engineering chops. You'll work closely with stakeholders to make sure the AI solutions actually solve real-world problems instead of just looking good on paper.

The Core Tech Stack

You need deep, production-level fluency in Python, SQL, and modern AI patterns like vector embeddings, RAG, and multi-agent workflows. Since this is a full-stack and enterprise environment, familiarity with PostgreSQL, React, and cloud-native deployments is critical. They aren't looking for someone who just calls OpenAI APIs in a Jupyter notebook; they want an engineer who knows how to monitor, evaluate, and scale AI models reliably in production.

Interview Expectations

Expect to be grilled on your past production failures, specifically around managing LLM latency, hallucination tracking, and cost control in live environments. The hiring manager is going to probe your system design skills by asking you to architect a real-time agentic RAG system from scratch on a whiteboard, watching closely to see how you balance infrastructure costs with user experience.

Application Advice

Make sure your resume doesn't read like a list of tutorials or API integrations. Explicitly highlight systems you built from 0 to 1 that went live into production, and use keywords like RAG, vector embeddings, agentic workflows, and AI observability to clear the ATS.