AlphaSense
Cloud Support Engineer
$100K–$111K
Remote - United States · Remote · Full-time · Posted Aug 20, 2026
- Experience
- 2 - 4 years in technical support, IT operations, cloud operations or a related field.
- Education
- Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent practical experience).
About the job
Provides second-line technical support for AlphaSense customers, investigating and resolving issues across SaaS and private cloud environments and coordinating with Support and Engineering.
What you'll do
- Own assigned customer cases through investigation, troubleshooting, escalation when needed, and resolution.
- Diagnose issues using logs, traces, and reproducible test cases; document and escalate out-of-scope issues with high-quality artifacts.
- Collaborate with Support, account teams, and R&D; use AI tools in daily work and contribute to runbooks, KEDB articles, and FAQs.
What they ask for
- Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent practical experience).
- Working proficiency with CLI tools and scripting skills in Python, JavaScript, or similar.
- Working knowledge of GraphQL, REST APIs, and MCP-related troubleshooting; familiarity with connecting LLMs to external tools, including multi-agent systems.
- Experience managing customer support cases through their lifecycle and ability to communicate technical concepts clearly.
Nice to have
- Experience troubleshooting networking, a major cloud platform (preferably AWS), and containerized environments using Kubernetes or Docker.
- Experience with Infrastructure as Code tools, search technologies and data stores, Java, or standard software release lifecycles.
Pay
$100,000 — $111,000 USD (base salary)
Tools
kubectl, awscli, Python, JavaScript, GraphQL, REST APIs, Prometheus, Grafana
AI focus
Use AI tools for technical investigations, log analysis, customer communication, and documentation; develop repeatable prompts and workflows, and apply AI fluency in day-to-day work. Familiarity with connecting LLMs to external tools, including multi-agent systems, is required.
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