E
AI Engineer - AIOps Lead
eTeam
2 hours ago
Full-time
On-site
AI Engineer – AIOps Lead
Work Location & Reporting Address: Dallas, TX or Bothell, WA Interview Mode- In-person Interview Contract duration: 6 Months Must Have Skills: Agentic AI / AI Agents development and orchestration LLM-based solution design and knowledge frameworks Azure Cloud and Kubernetes MuleSoft and REST API integrations AI-driven monitoring, support, and automation AI Ops Knowledge Enterprise application and cloud operations Telecom/OSS Domain is mandatory Skill Mix: 60% AI / Agentic AI, 40% Cloud & Enterprise Technology. Nice to Have Skills: RAG and AI knowledge management frameworks AIOps and intelligent automation solutions GenAI governance and observability Python automation and scripting Cloud-native architecture and DevOps practices Detailed Job Description: The role involves building intelligent AI agents capable of autonomous decision-making, incident analysis, remediation recommendations, workflow orchestration, and operational support across cloud-native ecosystems. The candidate will work closely with cloud, application, and platform engineering teams to deliver scalable AI-driven operations solutions. Key Responsibilities: Design and implement an Agentic AI framework for Cloud Infrastructure and Application Operations. Build and orchestrate AI agents for monitoring, diagnostics, knowledge retrieval, incident triaging, remediation, and operational automation. Develop knowledge frameworks utilizing enterprise documentation, operational runbooks, and support processes. Integrate AI agents with enterprise platforms and operational tools using APIs. Enable AI-driven automation for cloud monitoring, observability, application support, and operational intelligence. Collaborate with Cloud and Platform Engineering teams to architect scalable and secure AI-enabled solutions. Design reusable frameworks, accelerators, and governance models for AI-based operations. Support Azure cloud environments, Kubernetes platforms, MuleSoft integrations, and enterprise application ecosystems. Mentor engineering teams and drive adoption of AI-driven operational practices. Minimum Years of Experience: 8 to 10 years Certifications Needed: None
Work Location & Reporting Address: Dallas, TX or Bothell, WA Interview Mode- In-person Interview Contract duration: 6 Months Must Have Skills: Agentic AI / AI Agents development and orchestration LLM-based solution design and knowledge frameworks Azure Cloud and Kubernetes MuleSoft and REST API integrations AI-driven monitoring, support, and automation AI Ops Knowledge Enterprise application and cloud operations Telecom/OSS Domain is mandatory Skill Mix: 60% AI / Agentic AI, 40% Cloud & Enterprise Technology. Nice to Have Skills: RAG and AI knowledge management frameworks AIOps and intelligent automation solutions GenAI governance and observability Python automation and scripting Cloud-native architecture and DevOps practices Detailed Job Description: The role involves building intelligent AI agents capable of autonomous decision-making, incident analysis, remediation recommendations, workflow orchestration, and operational support across cloud-native ecosystems. The candidate will work closely with cloud, application, and platform engineering teams to deliver scalable AI-driven operations solutions. Key Responsibilities: Design and implement an Agentic AI framework for Cloud Infrastructure and Application Operations. Build and orchestrate AI agents for monitoring, diagnostics, knowledge retrieval, incident triaging, remediation, and operational automation. Develop knowledge frameworks utilizing enterprise documentation, operational runbooks, and support processes. Integrate AI agents with enterprise platforms and operational tools using APIs. Enable AI-driven automation for cloud monitoring, observability, application support, and operational intelligence. Collaborate with Cloud and Platform Engineering teams to architect scalable and secure AI-enabled solutions. Design reusable frameworks, accelerators, and governance models for AI-based operations. Support Azure cloud environments, Kubernetes platforms, MuleSoft integrations, and enterprise application ecosystems. Mentor engineering teams and drive adoption of AI-driven operational practices. Minimum Years of Experience: 8 to 10 years Certifications Needed: None