Applied AI Engineer — Production ML & LLMs (Hybrid NYC)

Logicbroker; New York (NY); On-site; USD 102,000 - 138,000; Full time

Job summary

Logicbroker is hiring its first AI Engineer in New York City, offering a hybrid work arrangement. The role focuses on solving high-priority AI engineering initiatives across the organization, requiring deep software engineering that extends to production-level AI/LLM work and detailed architectural discussions.

You will build end-to-end AI systems, partner with cross-functional teams, document work for long-term maintainability, and evaluate new AI tools with a bias toward robust, scalable

Qualifications

• Minimum of one year hands-on, applied AI/LLM development experience.
• Based in or willing to work on-site in New York City.
• Strong communication skills across stakeholders.

Responsibilities

• Take on high-priority AI engineering initiatives across the organization as needs arise.
• Architect and deliver production-quality software, with sound design decisions and technical trade-offs.
• Build applied AI/LLM systems—including agents, automations, and model integrations—end to end, with the ability to articulate the technical approach behind each.
• Partner directly with cross-functional stakeholders to scope initiatives efficiently and deliver durable systems.
• Document your work so that teams can maintain and extend it independently.
• Evaluate emerging AI tools and techniques against a rigorous engineering standard, prioritizing maintainability over novelty.

Skills

Applied AI/LLM development

Production software development

Cross-functional communication

Job description

Logicbroker is hiring its first AI Engineer in New York City, offering a hybrid work arrangement. The role focuses on solving high-priority AI engineering initiatives across the organization, requiring deep software engineering that extends to production-level AI/LLM work and detailed architectural discussions.

You will build end-to-end AI systems, partner with cross-functional teams, document work for long-term maintainability, and evaluate new AI tools with a bias toward robust, scalable