AI Engineer

🕒 October 7

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 12%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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ScaleOps - Cloud-Native Optimization

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11 - 50 employees

Founded 2022

☁️ SaaS

🤖 Artificial Intelligence

💰 $15M Series A on 2023-12

Cloud • SaaS • Artificial Intelligence

ScaleOps is a cloud-native optimization platform that focuses on automating real-time resource management for Kubernetes environments. By utilizing intelligent scheduling and automated rightsizing, ScaleOps enables businesses to reduce their Kubernetes costs by up to 80%, while enhancing cluster reliability and performance. Designed with DevOps in mind, ScaleOps allows teams to optimize CPU and memory allocation effortlessly and focus on strategic initiatives rather than manual configurations.

📋 Description

• Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions • Develop multi-agent systems for troubleshooting, optimization, FinOps, and how-to use cases • Use LLM capabilities including tool use, memory, and retrieval safely in production • Develop MCPs exposing ScaleOps capabilities to AI agents • Build integrations with Slack, Jira, Cursor, Windsurf, and other AI-powered IDEs • Build and deploy machine learning models for infrastructure policy detection, scaling prediction, and GPU configuration recommendations • Own the complete ML pipeline from training through production • Build internal AI tools for engineering, development, research, and support • Develop AI-powered tools for Sales and Support teams, including infrastructure analysis and cost optimization reports • Own AI systems from concept to production, targeting sub-2-second responses, reliability, safety, and cost-effectiveness • Build evaluation frameworks and implement security controls • Define AI architecture and best practices as a founding member of the AI team • Make technical decisions on frameworks, multi-agent systems, and data governance

🎯 Requirements

• Significant software engineering experience (typically 4+ years) • Strong Python skills and solid backend engineering fundamentals • Experience building and operating production systems in cloud environments • Practical experience bringing LLM-based systems into production, including handling latency, cost control, and failure modes • Familiarity with agentic frameworks such as LangChain and MetaGPT • Familiarity with evaluation frameworks • Strong ownership and ability to operate independently while collaborating closely across teams • Experience with structured or operational data such as configurations, logs, and metrics • Experience with retrieval systems (RAG) or vector databases (advantage)

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