Machine Learning Engineer

You will join a forward-thinking programme focused on transforming observability through AI and machine learning. This role offers the opportunity to build real-time anomaly detection models, work with modern MLOps practices, and directly impact how large-scale systems predict and prevent issues. You will gain hands‑on experience with advanced AIOps use cases, operate in a highly secure environment, and contribute to a production-level transformation of monitoring capabilities.

Responsibilities

• Build and deploy machine learning models for anomaly detection and alert correlation
• Transition monitoring from static thresholds to real‑time intelligent detection
• Support the integration of AI capabilities into the ELK stack
• Work within an MLOps framework to manage, monitor, and improve models
• Contribute to automated alert prioritisation and multi‑source correlation

Skills / Must have

• Strong experience in Machine Learning, particularly anomaly detection or time‑series analysis
• Hands‑on experience with Python or similar for ML development
• Experience deploying models into production environments
• Understanding of MLOps principles and tools
• Experience with ELK Stack (Elasticsearch, Logstash, Kibana)
• Opportunity to work on a cutting‑edge AIOps transformation programme
• Exposure to advanced AI, ML, and observability technologies
• High‑impact role with ownership of production‑level solutions
• Experience working in a secure, enterprise‑scale environment