AI/ML Architect

Are you passionate about applying Machine Learning and AI to solve real-world infrastructure challenges? We are seeking an experienced ML/AI Architect to help develop and operationalize predictive models that identify track nonconformities across the NYC subway system. Description: Seeking an ML/AI Architect to develop, fine-tune and operationalize machine learning models that predict track nonconformities on the NYC subway system. Working within a Google Cloud Vertex AI environment, you will leverage sensor data (vibration, audio, location) captured by Pixel phone hardware kits on revenue cars and ground-truth defect records from MTA's Hexagon system to build predictive models supporting the TrackInspect application. Must have exp on GCP(With data science background is added advantage ) Key Responsibiliti • esPerform feature engineering on sensor and operational data to identify patterns correlated with documented track defects in Hexagon (HxG • N)Leverage and refactor existing prototype ML models for pilot-scale use; validate performance and suitability for producti • onUtilize Vertex AI Workbench to train, retrain, and version ML models; track experiments and metri • csConduct hyperparameter tuning and model validation activities to optimize nonconformity prediction accura • cyDeploy trained models for daily batch inference; connect deployed models to batched sensor input pipelin • esImplement model monitoring, alerting, and a continuous feedback loop from Track Inspector inputs captured in Hx • GNBuild and maintain CI/CD pipelines and multi-environment deployment strategy for current mode • lsBuild the BigQuery 'Predictions – User Friendly View' incorporating location attributes, prediction metadata, and feedback fiel • dsAssess model results against known track defects within HxGN and present findings at model evaluation meetings with M • TAProficiency in Python (scikit-learn, TensorFlow or PyTorch) and S • QLHands-on Vertex AI experience (Workbench, training pipelines, model deploymen • t)Strong understanding of feature engineering, model validation, and hyperparameter optimizati • onExperience with time-series or sensor data (vibration, audio, or accelerometer signals a plu • s)Familiarity with BigQuery as a data platform for ML pipelin • esExperience with geospatial ML or GPS/LRS-based positioning mode • lsPrior work in predictive maintenance, anomaly detection, or IoT sensor analyti • csGoogle Professional ML Engineer certificati • onUnderstanding of transit or infrastructure asset management syste • msExperience with Looker Studio or similar BI tools for ML output visualizati on