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AI SRE Anomaly Detection Developer

Caleb Rodriguez

Full-time · Job · London

About the role

Static alert thresholds are the bane of every on-call engineer's existence. Set them too tight and you get paged at 3 AM for normal traffic fluctuations. Set them too loose and you miss the real incidents. Our observability platform replaces static thresholds with ML-powered anomaly detection that learns what "normal" looks like for each metric and adapts as systems evolve. We process 14 billion time series data points per day across 120 enterprise clients. The anomaly detection models need to handle everything from smooth CPU utilization curves to bursty API error counts to seasonal e-commerce traffic. One-size-fits-all doesn't work — you'll build a system that automatically selects the right detection algorithm for each metric's characteristics. The ML team is four engineers. You'll own the model selection and training pipeline that keeps detection accurate as client infrastructure changes. The hardest part isn't building good detectors — it's building a system that stays good without constant manual tuning.

Responsibilities

  • Build automated model selection for diverse time series characteristics
  • Design training pipelines that adapt to changing infrastructure patterns
  • Implement false positive reduction without sacrificing true positive detection
  • Create evaluation frameworks using labeled incident data from clients
  • Optimize detection latency for real-time alerting requirements
  • Support client onboarding with custom detection configuration

Requirements

  • 3+ years in ML engineering with time series or anomaly detection experience
  • Familiarity with statistical and ML-based anomaly detection approaches
  • Strong Python skills with experience in production ML pipelines
  • Understanding of SRE/DevOps observability concepts and tooling
  • Experience with Kubernetes and cloud infrastructure monitoring

Benefits

  • Fully remote across UK/EU time zones
  • 28 days PTO plus bank holidays
  • Private health insurance
  • £2,500 learning and conference budget
  • Flexible working hours

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Categories

MLOps & AI Infrastructure data science & analytics

Skills

python pytorch docker kubernetes aws

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure data science & analytics

Skills

python pytorch docker kubernetes aws sql

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