Giant Leap Trainee in Machine Learning Automation Development at Vaisala, working on real-life industry projects in hybrid role. Collaborate with experts and contribute to weather sensor technology development.
Responsibilities
Productify and extend an existing proof-of-concept outage/exception classifier into a robust & extendable production service
Refine a pipeline that ingests critical-state alerts from monitoring services, enriches them with xWeather data & internal telemetry, and outputs: likely root cause confounding factors (weather, network conditions, sensor patterns) recommended troubleshooting / recovery steps
Develop and maintain a full-stack LAMP implementation supporting the ensemble service gestalt (API layer, backend, persistence, and UI/ops views as needed)
Implement deterministic ML / rules + trend-based inference using historical time series and known failure modes
Integrate results into operational workflows to support rapid triage and reduce context-switching for NOC operators (supporting “0-click tickets” style automation)
Design for scale across networks such as lightning, WindCube lidar, weather radar, airports, and other observation systems
Collaborate closely with NOC stakeholders to validate outputs, tune inference logic, and ensure the service improves real-world resolution time
Requirements
Strong experience building full-stack LAMP services (Linux/Apache/MariaDB/MongoDB/Python)
Solid Python REST microservice development experience; FastAPI / Flask
Experience working with time series data (ingestion, normalization, trend detection, anomaly/exception patterns)
Practical approach to “deterministic machine learning” / explainable inference (rules, heuristics, feature-based classification, or lightweight models where behavior is understandable and supportable)
Ability to ship production software: testing, logging/observability, reliability, clear APIs, and attention to comprehensive documentation
Comfort collaborating with operations teams and iterating quickly based on feedback from diverse sources.
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