Back End Engineer, AI Systems

Salt Digital Recruitment; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Salt Digital Recruitment is seeking a Backend Engineer - AI Systems to own inference and orchestration layers powering AI interactions across the platform. You will design and operate production systems linking AI models with backend infrastructure for fast, reliable services used by mobile and desktop apps.

You will work on latency, scalability, and observability while collaborating with AI/ML teams to bring new capabilities into production.

Qualifications

• Strong backend engineering fundamentals and production systems experience.
• Experience delivering high-throughput, low-latency services at scale.
• Familiarity with AI inference patterns including LLMs and multimodal models.

Responsibilities

• Build and operate backend systems supporting AI-powered features in production.
• Design scalable inference pipelines, orchestration layers and service boundaries around AI models.
• Develop reliable APIs that integrate with frontend and ML systems.
• Optimize latency and throughput across inference, caching, batching and streaming.
• Own production concerns including monitoring, logging, alerting and incident response.
• Troubleshoot complex issues across distributed systems under load.
• Improve system performance using production data and usage metrics.
• Collaborate with engineering and AI/ML teams to bring new AI capabilities to production.

Skills

Backend systems

Low-latency services

Distributed systems

API design

Performance optimization

Observability

ML/AI collaboration

Tools

Kubernetes

Docker

PyTorch

SQL

NoSQL

LLM frameworks

Job description

About the Opportunity

We are partnering with a fast-growing technology company building a new generation of AI-native applications designed to make everyday tasks, communication, organization and workflows more intelligent and intuitive. The team is focused on creating proactive AI experiences that require minimal prompting while maintaining high levels of reliability, persistent context and real-world task completion. This is an opportunity to work on highly scalable AI infrastructure where performance, reliability and user experience are critical.

The Role

As a Backend Engineer - AI Systems, you will help own the inference and orchestration layer powering AI interactions across the platform. Sitting at the intersection of AI models, backend infrastructure and end users, you will build production systems that transform model capabilities into fast, stable and observable services used across mobile and desktop applications. You will be responsible for designing and operating systems where latency, scalability, reliability, correctness and cost have a direct impact on the end-user experience.

What You'll Be Doing

• Build and operate backend systems supporting AI-powered features in production.
• Design scalable inference pipelines, orchestration layers and service boundaries around AI models.
• Develop reliable APIs that integrate effectively with frontend and machine learning systems.
• Optimize latency and throughput across inference, caching, batching and streaming.
• Own key production concerns including monitoring, logging, alerting and incident response.
• Troubleshoot and resolve complex issues across distributed systems operating under load.
• Continuously improve system performance and reliability based on real-world usage and production data.
• Work closely with engineering and AI/ML teams to bring new AI capabilities into production.

What We're Looking For

• Strong backend engineering fundamentals with experience building and operating production systems.
• Experience developing high-throughput, low-latency services at scale.
• Familiarity with AI inference patterns including LLMs, embeddings and multimodal models.
• Experience working with distributed systems and debugging complex production environments.
• Strong understanding of API design, system reliability and performance optimization.
• Comfortable working in a fast-moving environment where experimentation and iteration are encouraged.
• A hands‑on mindset with a strong bias toward shipping, measuring and improving production systems.

Technology Environment

• Python
• Node.js
• PyTorch
• Commercial and open-source LLMs
• SQL and NoSQL databases
• Kubernetes
• Docker
• AI inference and orchestration frameworks
• Distributed systems and cloud infrastructure

What Success Looks Like

You will help build AI backend systems capable of operating reliably at scale while maintaining low latency and high throughput. APIs and services will provide stable integration points across AI, machine learning and frontend systems, while strong observability and operational practices will enable production issues to be quickly identified and resolved. Over time, you will use real-world usage and system performance data to continuously improve the speed, reliability and scalability of the platform.