AI Ops Engineer

BlackCube Labs; Dallas (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

BlackCube Labs seeks an AI Ops Engineer to design and deploy agentic AI systems for cloud and application operations, enabling autonomous decision-making, incident analysis, and remediation workflows.

You will collaborate with Cloud and Platform teams to deliver scalable AI-driven operations across Azure, Kubernetes, and enterprise environments, ensuring secure, observable, and automated processes.

Qualifications

• Must have experience designing and building agentic AI / AI agents and orchestration.
• LLM-based solution design and knowledge frameworks experience.
• Experience with Azure Cloud and Kubernetes environments.
• Experience with MuleSoft integrations and REST APIs.

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 automation.
• Develop knowledge frameworks using runbooks and enterprise docs.
• Integrate AI agents with enterprise platforms and tools via APIs.
• Enable AI-driven automation for cloud monitoring, observability, and operational intelligence.
• Collaborate with Cloud and Platform teams to architect scalable, secure AI-enabled solutions.
• Create reusable frameworks, accelerators, and governance for AI-based operations.
• Support Azure, Kubernetes, MuleSoft, and enterprise ecosystems.

Skills

Agentic AI

LLM-based design

AI-driven monitoring

AI Ops Knowledge

Enterprise cloud operations

Telecom/OSS domain

Tools

Azure Cloud

Kubernetes

MuleSoft

REST API integrations

Job description

Job Title: AI Ops Engineer
Work Location: Dallas, TX or Bothell, WA
Contract duration: 06 Months

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.

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

Minimum Years of Experience: 6 to 7 years