Staff AI Engineer (GenAI)

TL;DR

Staff AI Engineer (GenAI/Python): Leading the design and delivery of complex AI/ML systems and agentic architectures with an accent on scalability, reliability, and production-grade integration. Focus on building RAG pipelines, optimizing token economics, and establishing engineering standards for generative AI.

Location: Remote (Must be based in the US)

Salary: $177,375 – $209,625 USD

Company

A digital agency combining engineering and creativity to build production-ready AI systems that integrate into enterprise operations for their clients.

What you will do

• Lead end-to-end design and implementation of complex ML/AI systems, owning architecture decisions from research to production.
• Build and evolve scalable ML platforms, pipelines, and infrastructure to support repeatable model deployment.
• Partner with product and leadership to shape AI strategy and align technical direction with business outcomes.
• Define AI architecture and engineering standards, focusing on responsible AI practices and long-term scalability.
• Mentor junior and mid-level engineers through coaching, code reviews, and technical pairing.
• Drive the delivery of high-stakes agentic systems with a focus on reliability, cost, and safety.

Requirements

• Must be based in the US.
• 7+ years of professional software engineering experience, with 4+ years focused on production AI/ML systems.
• Expert proficiency in Python and deep hands-on experience with generative AI development.
• Deep expertise with AWS services and AWS GenAI offerings.
• Proven track record of shipping complex agentic systems and RAG pipelines (chunking, embedding, vector databases).
• Strong knowledge of AI safety, PII handling, and containerized deployments using Docker and Kubernetes.

Nice to have

• Expert day-to-day knowledge of AI-forward coding tools such as Claude Code and Cursor.

Culture & Benefits

• High-velocity environment shipping production-ready AI in 30 to 45 days.
• Culture that balances technical engineering rigor with creative design.
• Direct impact on enterprise-scale AI products and operational workflows.
• Opportunity to establish engineering standards for a scaling AI practice.