AI Engineer

🕒 October 7

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 12%

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ScaleOps

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2022

🤝 B2B

📣 Marketing

B2B • Marketing

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📋 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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