Find your next AI Engineering role
2+ open roles · 22+ companies hiring
2 open positions
AI Applications Ops Lead, GPS
Scale AI
Scale's Global Public Sector team is focused on using AI to address critical challenges facing the public sector worldwide. This role involves designing and developing the production lifecycle of full-stack AI applications, ensuring end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. The goal is to enable the public sector to transform operations and better serve citizens through cutting-edge technology, with the opportunity to be a founding member of the team.
Principal AI Ops Architect, GPS
Scale AI
Scale's Global Public Sector team is dedicated to leveraging AI to tackle significant challenges within the public sector worldwide. This involves creating custom AI applications impacting millions, generating high-quality training data for national LLMs, and providing AI upskilling and advisory services. As a Principal AI Ops Architect, you will be instrumental in designing and developing the production lifecycle for full-stack AI applications. Your role will encompass ensuring end-to-end system reliability, real-time inference observability, sovereign data orchestration, secure software integration, and the resilient cloud infrastructure necessary for international government partners. At Scale, we empower the public sector to transform operations and enhance citizen services through advanced technology, and we are looking for individuals ready to shape the future of AI in this domain.
Frequently asked questions
What counts as an AI Engineering job?
AI Job Board lists engineering roles that build, deploy, or operate AI/ML systems — including LLM engineering, RAG (retrieval-augmented generation), AI agents, prompt engineering, AI infrastructure and MLOps, model serving/inference, and fine-tuning. It excludes non-engineering functions like AI sales or marketing roles.
What is the difference between an AI Engineer and a Machine Learning Engineer?
An AI Engineer typically builds applications on top of existing models — LLM integration, RAG pipelines, agent orchestration, and prompt design. A Machine Learning Engineer more often trains, fine-tunes, or productionizes custom models. Many companies use the titles interchangeably, so search both when browsing.
Does AI Job Board include AI infrastructure and MLOps roles?
Yes. Titles such as AI Infrastructure Engineer, ML Platform Engineer, GPU Infrastructure Engineer, Inference Engineer, MLOps Engineer, and AI Site Reliability Engineer are all covered — these roles focus on the systems that serve and scale AI models rather than building models themselves.
Are remote AI and ML jobs available?
Yes. Filter by "Remote" on the jobs page to see fully remote AI/ML engineering roles, or browse the remote jobs feed directly.
What skills are most in demand for AI engineering roles?
The most commonly requested skills are LLM APIs (OpenAI, Anthropic, Gemini), RAG and vector databases (Pinecone, Weaviate, Qdrant), agent frameworks (LangGraph, CrewAI, AutoGen), inference/serving tools (vLLM, Ray Serve, TensorRT-LLM), and fine-tuning. Use the skill filters on the jobs page to browse by specific technology.
How fresh are the job listings?
Listings are sourced continuously from company career pages and refreshed automatically. Each job shows when it was posted, and the sitemap and API expose last-updated timestamps for every posting.
How much do AI Engineers get paid?
Compensation varies widely by role, seniority, and location. Where employers disclose a salary range, it is shown directly on the job listing — filter by salary range on the jobs page to narrow results to your target compensation.