AI Backend Systems Engineer

Blue Signal

Mount Pleasant Township, PA, United States

10 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.juju.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Business Logic Systems Engineering Cloud Computing Databases Data Stores Relational Databases Distributed Systems Python (Programming Language) Language Modeling Node.Js

+14 more

Open Source Technology OpenAI Systems Integration Workflow Management Systems Pytorch Large Language Models Reliability of Systems Backend Kubernetes Machine Learning Operations Virtual Agents Api Design Model Inference Docker

Job description

Build scalable backend infrastructure that powers AI-driven applications and enterprise automation workflows.
Shape model-serving and workflow coordination components that connect inference, application logic, data, and external services.
Tune service performance across request routing, caching, batching, streaming, and model calls with close attention to response time, capacity, availability, and spend.
Create durable interfaces and service contracts that allow client applications, data stores, AI components, and third-party tools to work together cleanly.
Establish strong operational visibility through telemetry, diagnostics, alerting, and disciplined response to production issues.
Diagnose issues within distributed environments, determine underlying causes, and use real-world performance data to strengthen system reliability.
Turn loosely defined product or operational needs into practical technical plans, then carry solutions from design through release and refinement.
Partner across engineering and business functions while independently driving technically ambiguous work to completion.

Requirements

Proven backend engineering experience delivering dependable, scalable production services.
Hands-on success with high-volume, latency-sensitive systems and troubleshooting distributed architectures during heavy demand.
Practical experience with production AI software, such as LLM or model inference, workflow orchestration, model-facing APIs, or intelligent automation.
Working knowledge of Python, Node.js, PyTorch, relational and non-relational data stores, Kubernetes, and Docker.
Experience integrating commercial or open-source language models, including platforms from OpenAI or Anthropic.
Strong command of API design, databases, cloud infrastructure, and scalable systems architecture.
Demonstrated ownership, sound engineering judgment, and accountability for outcomes in fast-moving, uncertain environments.
Clear communication with technical and non-technical partners, paired with a strong bias toward shipping quality software and learning from real-world usage.

Benefits & conditions

Competitive compensation and the opportunity to contribute to high-impact AI initiatives.
Fully remote opportunity available to qualified candidates nationwide.
Hands-on ownership of technically challenging systems from architecture through production operation.
Exposure to modern AI infrastructure, model integrations, distributed services, and cloud-native technologies.
A highly technical environment where engineering judgment, execution, and quality matter more than pedigree.
Opportunity to solve complex scalability and reliability challenges as AI capabilities move from experimentation into production.

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