AI Observability Engineer

Role Summary

We are seeking an Observability Automation Engineer to design, implement, and support enterprise observability platforms while driving automation, AI, and Agentic AI initiatives.

Key Responsibilities

• Design, deploy, and maintain enterprise observability platforms for monitoring, logging, tracing, and alerting.
• Develop dashboards, KPIs, and service health metrics to provide actionable operational insights.
• Implement and optimize observability solutions using tools such as Splunk, AppDynamics, or Splunk Observability Cloud platforms.
• Automate operational processes, alert management, health checks, and incident response workflows using scripting and orchestration tools.
• Collaborate with engineering and operations teams to improve application performance, reliability, and scalability.
• Analyze incidents, identify root causes, and implement preventive measures through proactive monitoring and automation.
• Drive adoption of AI-powered observability capabilities, including anomaly detection, predictive analytics, event correlation, and intelligent alerting.
• Leverage Microsoft Copilot, Generative AI, and automation technologies to enhance troubleshooting, operational efficiency, and engineering productivity.
• Develop AI-assisted runbooks, knowledge bases, and self-healing solutions to reduce manual intervention and Mean Time to Resolution (MTTR).
• Design and implement AI-driven observability solutions using telemetry, monitoring, logging, and distributed tracing platforms.
• Develop automated remediation, self-healing workflows, and operational runbooks using scripting, orchestration, and infrastructure-as-code tools.
• Build and integrate Agentic AI solutions that can autonomously analyze alerts, retrieve operational context, recommend actions, and execute approved remediation workflows.
• Leverage Microsoft Copilot and Generative AI tools to improve incident investigation, root-cause analysis, knowledge management, and engineering productivity.
• Implement AI-powered anomaly detection, event correlation, capacity forecasting, and predictive monitoring capabilities.
• Develop integrations between observability platforms and AI agents to automate repetitive operational tasks and improve MTTR.
• Collaborate with application, SRE, cloud, and platform teams to identify opportunities for AI-assisted operations and process automation.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or related field.
• 3+ years of experience in Observability, Site Reliability Engineering (SRE), Infrastructure Engineering, or Operations Engineering.
• Strong experience with monitoring, logging, tracing, and performance management platforms.
• Proficiency in scripting and automation using Python, PowerShell, Bash, or similar languages.
• Experience working with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
• Knowledge of CI/CD pipelines, Infrastructure as Code (Terraform, Bicep, ARM, etc.), and DevOps practices.
• Experience with Python, PowerShell, or similar languages for automation and API integrations.
• Experience developing automation workflows and integrating observability platforms with cloud services and operational tools.
• Familiarity with AI-assisted engineering practices and Copilot-enabled development workflows.
• Strong analytical and troubleshooting skills.

Preferred Qualifications

• Experience with Microsoft Copilot, Azure OpenAI, Copilot Studio, or other AI-powered engineering tools.
• Knowledge of AIOps platforms and machine learning concepts related to observability.
• Experience implementing OpenTelemetry standards and distributed tracing solutions.
• Familiarity with Kubernetes, containers, and cloud-native monitoring architectures.
• Experience building automated remediation and self-healing workflows.
• Hands-on experience with Microsoft Copilot, Azure AI Services, Azure OpenAI, Copilot Studio, LangChain, Semantic Kernel, Agentic AI frameworks, or similar technologies.
• Experience building AI agents, retrieval-based knowledge systems, AI-powered chatbots, or autonomous operational workflows.
• Knowledge of RAG architectures, vector databases, prompt engineering, and AI governance best practices.
• Familiarity with AIOps platforms and event intelligence solutions.