Senior Engineer- Infra Monitoring

Overall objectives

•To ensure the continuous, proactive, and intelligent monitoring of IT infrastructure through the integration and operation of modern observability tools.

•To develop and operationalise machine learning-based anomaly detection mechanisms for early detection of issues across compute, network, storage, and application layers.

•To support incident prevention and reduction of MTTR (Mean Time to Resolution) through predictive insights, automated alerts, and root cause correlation.

•To enhance operational visibility, reliability, and resilience of critical infrastructure components by applying modern data-driven monitoring strategies.

Role specific responsibilities

•Design, implement and fine-tune infrastructure monitoring solutions across on-prem and cloud platforms.

•Develop ML-driven anomaly detection pipelines using telemetry data (logs, metrics, traces).

•Integrate observability data into a unified dashboard and alerting platform with meaningful visualisations and thresholds.

•Continuously train and evaluate ML models to reduce false positives and increase signal accuracy.

•Collaborate with incident management teams to define actionable alerts and automated remediation triggers

General functional responsibilities

•Ensure compliance with enterprise standards, regulatory controls, and audit requirements related to monitoring and data collection.

•Maintain documentation of monitoring architecture, detection rules, ML models, and escalation paths.

•Work closely with infrastructure, application, and security teams to improve data ingestion and correlation.

•Contribute to the continuous improvement roadmap for observability maturity (e.g., from reactive to predictive monitoring).

•Mentor junior team members on observability tools, ML practices, and operational excellence.

•Provide out-of-hours support for major incidents when required, as part of a rota.

Core competencies required

•Strong knowledge of infrastructure monitoring tools (e.g., Prometheus, Grafana, Dynatrace, Datadog, Splunk, New Relic).

•Deep understanding of telemetry data (metrics, logs, traces) and how they relate to system performance and health.

•Experience with ML models for anomaly detection (supervised/unsupervised learning, clustering, time-series forecasting).

•Understanding of AIOps frameworks and concepts.

•Good grasp of core infrastructure (Linux/Windows servers, VMs, containers, cloud instances).

•Familiarity with networking, databases, storage systems and cloud-native environments (AWS, Azure).

•Analytical mindset with a bias for root cause analysis.

•Effective communicator able to bridge engineering and operations teams. 

•Proactive problem-solver with ownership mentality.