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Tech Stack
Tools & technologiesBigQueryCloudNumpyPandasPythonScikit-Learn
About the role
Key responsibilities & impact- Explore historical telemetry and operational data, assemble labels and engineer features, including detection baselines
- Build a statistical baseline detector and ML anomaly model; evaluate, threshold and tune both against shadow-mode output before production
- Serve models in near real time and instrument them with model monitoring and shadow-mode tracking
- Design and train models detecting granular partial service degradation against expected performance rather than fixed thresholds
- Upgrade the initial outage detector to a supervised model using labels generated in production
- Build MLOps foundations including model registry, retraining triggers, champion/challenger comparison, drift detection and automated evaluation
- Develop an internal AI agent with retrieval, tool use and guardrails, supported by evaluation harness, red-teaming and explicit refusal policy
- Collaborate with data engineering on feature pipelines and backend engineering on model output, impact scoring and remediation
- Work as part of a delivery squad of approximately nine people using Jira, Git and CI/CD
Requirements
What you’ll need- Several years of hands-on experience taking ML models from exploration to production
- Strong Python and core data science stack (pandas, NumPy, scikit-learn or similar)
- Practical experience with anomaly detection or time-series modelling, including thresholding and dealing with noisy or sparse labels
- Solid understanding of model evaluation, monitoring and drift in a live environment
- Experience with MLOps tooling and practices: model registries, experiment tracking, automated retraining and CI/CD for models
- Experience on a major cloud platform, ideally Google Cloud (e.g., Vertex AI, BigQuery)
- Ability to explain model behaviour and trade-offs clearly
- Nice to have: experience building LLM applications with retrieval-augmented generation, tool use, guardrails, evaluation and red-teaming
- Nice to have: background in telecoms, networking or large-scale operational data
- Nice to have: experience with shadow deployment, canary rollout or champion/challenger testing of models
- Nice to have: familiarity with geospatial or hierarchical data
Benefits
Comp & perks- Diversity, inclusion, and belonging initiatives
- Support and engagement with local communities through meaningful initiatives promoting equity and opportunity