AI and Analytics Developer- Network & Infrastructure Observability

 Role Overview: 
We are seeking a skilled AI and Analytics Developer to join our Network & Infrastructure Observability team. This role focuses on developing intelligent analytics and automation solutions to improve visibility, performance, and operational efficiency across hybrid cloud environments. Key Responsibilities: 
Develop AI/ML models to detect anomalies, predict failures, and optimize network and infrastructure performance. Design and implement data pipelines to collect, process, and analyze telemetry, logs, metrics, and events from diverse infrastructure sources. Build dashboards and visualizations for real-time observability, root cause analysis, and performance insights. Collaborate with cross-functional teams (Cloud, Network, Security, DevOps) to identify observability gaps and develop data-driven solutions. Integrate observability tools (e.g., Prometheus, Grafana, ELK, Splunk, Datadog) with custom analytics workflows. Apply statistical and machine learning techniques to derive actionable insights from structured and unstructured data. Ensure scalability, reliability, and security of analytics solutions in production environments. Document processes, models, and systems for knowledge sharing and compliance. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise

Hands-on experience with  IBM Cloud  for building and deploying cloud-native analytics solutions.

Exposure in  IBM Network Intelligence  or similar platform for analyzing network telemetry and performance data.

Experience with  IBM Cloud Pak for AIOps (CP4AIOps)  for intelligent incident detection, correlation, and resolution.

Strong understanding of  WatsonX.ai  for developing and training AI/ML models tailored to infrastructure and network use cases.

Familiarity with  WatsonX Orchestrate  for automating workflows and enhancing operational efficiency.

Programming skills in Python or similar languages, with experience in AI/ML frameworks.

Solid grasp of hybrid cloud infrastructure, observability principles, and data engineering.

Preferred technical and professional experience

IBM certifications in Cloud, AIOps, or WatsonX technologies.

Experience integrating observability tools with enterprise IT operations.

Knowledge of MLOps and CI/CD practices for model deployment and lifecycle management.