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AI Root Cause Analysis Automation Engineer

Caleb Rodriguez

Full-time · Job · London

About the role

During a P1 incident, every minute of mean time to resolution costs real money. Our platform monitors 220 enterprise customers' production environments, collecting 800GB of telemetry per day. When something breaks, the on-call engineer opens a dashboard and starts investigating. The problem: they spend 40 minutes reading logs and correlating signals before they understand what happened. We want to cut that to 5 minutes. Root cause analysis automation is genuinely hard. Symptoms appear in one service; the cause is three hops upstream. The causal graph changes with every deployment. Standard approaches — threshold alerts and runbook lookups — miss the non-obvious correlations. We're building a system that uses causal inference, anomaly correlation, and LLM-assisted log analysis to generate a ranked hypothesis list that's right the first time 75% of the time. You'll own this system. You'll design the causal inference framework, build the anomaly correlation engine, integrate the LLM analysis layer, and measure the system against historical incident records where the true root cause is known. This is a staff-level problem requiring both ML depth and production operations judgment.

Responsibilities

  • Design a causal inference framework for production incident root cause analysis
  • Build anomaly correlation engine across distributed telemetry signals
  • Integrate LLM-assisted log analysis for hypothesis generation and ranking
  • Evaluate system accuracy against historical incident ground truth records
  • Optimize inference latency to produce hypotheses within 60 seconds of incident detection
  • Work with customer SRE teams to validate and refine hypothesis quality

Requirements

  • 7+ years in ML engineering or SRE with production observability experience
  • Experience with causal inference, time series correlation, or anomaly detection at scale
  • Strong Python and system design skills
  • Deep understanding of distributed systems and common failure patterns
  • Track record reducing MTTR through automated tooling

Benefits

  • Fully remote across UK/EU time zones
  • Top-of-market compensation
  • 30 days PTO
  • Equity participation
  • Annual $6,000 professional development budget

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python pytorch docker kubernetes aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python pytorch docker kubernetes aws fastapi

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