Applied AI Engineer: Backend & Multimodal Data Systems

Nxt Level; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Nxt Level in San Francisco is hiring an Applied AI Engineer to build production-grade AI systems for manufacturing data. This backend-focused role emphasizes multimodal data pipelines, retrieval and reasoning systems, and robust production APIs to unlock real-time operational intelligence.

You'll own systems end-to-end—from architecture and implementation to deployment, monitoring, and continuous improvement—collaborating with customers and product teams to translate real-world challenges into

Qualifications

• Backend design and build for production AI applications.
• Ingest multimodal manufacturing data.
• Build retrieval, reasoning, and data processing systems.
• Design scalable APIs and production-grade systems.

Responsibilities

• Design and build backend infrastructure for production AI applications.
• Develop multimodal data ingestion pipelines that process complex manufacturing data.
• Build retrieval, reasoning, and data processing systems that power operational intelligence.
• Design reliable, scalable APIs that connect AI systems to real-world workflows.
• Create evaluation and feedback loops to measure and improve model performance.
• Own system reliability, including logging, monitoring, diagnostics, and error handling.
• Work directly with customers and product teams to translate manufacturing challenges into technical solutions.
• Take ownership of engineering decisions from initial design through production.

Skills

TypeScript

Backend engineering

APIs

Data pipelines

Cloud infrastructure

LLMs

Observability

Job description

Nxt Level in San Francisco is hiring an Applied AI Engineer to build production-grade AI systems for manufacturing data. This backend-focused role emphasizes multimodal data pipelines, retrieval and reasoning systems, and robust production APIs to unlock real-time operational intelligence.

You'll own systems end-to-end—from architecture and implementation to deployment, monitoring, and continuous improvement—collaborating with customers and product teams to translate real-world challenges into