About This Role
This Core Infrastructure Engineer role focuses on AIOps Engineering, designing, building, and scaling intelligent operational platforms. The position combines machine learning, LLMs, observability, automation, and service reliability to enable predictive and autonomous operations in a globally distributed environment.
Responsibilities
- Architect and implement AIOps capabilities such as intelligent incident routing, anomaly detection, operational copilots, ChatOps workflows, and automated remediation
- Design and build AIOps models using LLMs or classical ML for anomaly detection, correlation, root-cause identification, and intelligent event clustering
- Develop operational copilots and chatbots capable of responding to incidents, surfacing insights, and driving automation through natural language
- Build and maintain feature pipelines using telemetry, logs, metrics, traces, and runtime state for operational intelligence use cases
- Implement predictive and preventive operations use cases, including capacity forecasting, early warning systems, and noisy-neighbor detection
- Build knowledge-grounding systems for operational copilots using runbooks, incident data, historical patterns, service maps, and topology
- Integrate LLM-based reasoning into observability and automation platforms
- Develop embeddings, retrieval systems, RAG pipelines, and intent classification capabilities for operational queries
- Build automated workflows for incident triage, diagnostics, collaboration, and remediation
- Architect closed-loop automation patterns connecting alerts, insights, actions, and verification
- Develop reusable automation modules integrated with unified observability platforms, cloud platforms, and orchestration systems
- Integrate AIOps models with observability platforms handling logs, metrics, traces, events, and topology data
- Design real-time inference systems for high-volume telemetry streams
- Partner with SRE and platform teams to ensure pipelines, data contracts, and instrumentation support future AIOps workloads
- Work with transformation teams to define AIOps onboarding patterns, enablement models, and implementation guidelines
- Drive AIOps adoption across multiple products and platforms, ensuring reliability, scalability, and continuous improvement
- Participate in architecture reviews, data modeling discussions, and SRE transformation initiatives
Requirements
- 3+ years of experience in System Design, Platform & reliability engineering, ML engineering, data engineering, or AIOps-oriented roles
- Strong hands-on experience building ML or LLM-based systems using Python, Java, PyTorch, TensorFlow, or modern LLM frameworks
- Experience building automation workflows using tools such as StackStorm, Rundeck, Airflow, Jenkins, or cloud-native orchestration platforms
