Principal AI Engineer (Generative AI)

TL;DR

Principal AI Engineer (Generative AI): Set the technical direction and hands-on build for complex Generative AI and agentic systems, with an accent on production-grade architecture and evaluation/observability/responsible AI. Focus on scaling agentic workflows, deploying foundation-model and applied-ML solutions, and solving real-world constraints like latency, reliability, cost, and governance.

Location: Remote

Company

hirify.global builds production-grade Generative AI and agentic solutions for high-stakes client environments.

What you will do

• Own end-to-end architecture for the most complex GenAI and agentic systems, including standards for evaluation, observability, and responsible AI.
• Build hands-on with foundation models, embeddings fine-tuning, advanced RAG, semantic search, and novel agentic workflows using Python on Google Cloud.
• Engineer for production by applying MLOps discipline to ensure systems are served efficiently, monitored, and continuously improved.
• Architect and operate multi-step reasoning and agentic workflows at scale with design/verification to prevent cascading failures.
• Serve as technical authority in client engagements by translating strategy into AI architecture and shaping pre-sales solutions.
• Elevate engineers through architecture reviews, mentorship, and setting a high, teachable bar for AI-augmented engineering.

Requirements

• 10+ years of software/AI/ML engineering with a strong track record delivering AI systems to production at scale.
• Proven experience deploying GenAI and/or agentic products into production environments.
• Demonstrated technical leadership: owning architecture and setting direction across engagements or teams.
• Strong data engineering and SQL knowledge.
• Mastery of Python and shell scripting, plus fluency across the modern AI engineering stack.
• Senior client-facing experience translating technical complexity into business value for executive stakeholders.

Culture & Benefits

• Remote workplace.
• AI treated as a software engineering discipline with emphasis on production reliability and responsible AI.
• Hands-on role with ownership of outcomes at scale.
• Compensation aligned to final level, experience, expertise, interview performance, and geographic location (where applicable).