Data Science Architect Data Center AI Operations (Pune)

Data Science Architect Data Center AI Operations We are looking for a Data Science Architect to design and own the AI/ML platform strategy for next-generation data center operations management. This role will define how intelligence is embedded into infrastructure operations management for a world-class hyperscale data center operator. Key Responsibilities - Architect end-to-end AI/ML platforms for data center operations: predictive maintenance, thermal optimization, power efficiency (PUE/WUE), and capacity planning - Design AIOps pipelines integrating DCIM, BMS, SCADA, and CMDB data sources - Evaluate and standardize ML frameworks, feature stores, model registries, and MLOps tooling - Guide data scientists on model design, validation methodology, and production deployment - Define data quality, lineage, and observability standards for operational data - Engage with stakeholders to translate operational challenges into AI use cases and roadmaps Must-Have Profile - 14-18 years; 5+ years in data science / ML architecture roles - Solid background in time-series analysis, anomaly detection, or reinforcement learning for physical systems - Experience in industrial IoT, data center, or critical infrastructure analytics is a strong plus - Proficient in Python, ML frameworks (TensorFlow/PyTorch/XGBoost), and cloud ML platforms (Azure ML, AWS SageMaker) - Familiarity with DCIM platforms (Schneider EcoStruxure IT, Nlyte, or Sunbird) desirable Note- Please apply via our official careers portal only, as applications sent directly to executives may not be considered. Data Science Architect Data Center AI Operations We are looking for a Data Science Architect to design and own the AI/ML platform strategy for next-generation data center operations management. This role will define how intelligence is embedded into infrastructure operations management for a world-class hyperscale data center operator. Key Responsibilities - Architect end-to-end AI/ML platforms for data center operations: predictive maintenance, thermal optimization, power efficiency (PUE/WUE), and capacity planning - Design AIOps pipelines integrating DCIM, BMS, SCADA, and CMDB data sources - Evaluate and standardize ML frameworks, feature stores, model registries, and MLOps tooling - Guide data scientists on model design, validation methodology, and production deployment - Define data quality, lineage, and observability standards for operational data - Engage with stakeholders to translate operational challenges into AI use cases and roadmaps Must-Have Profile - 14-18 years; 5+ years in data science / ML architecture roles - Solid background in time-series analysis, anomaly detection, or reinforcement learning for physical systems - Experience in industrial IoT, data center, or critical infrastructure analytics is a strong plus - Proficient in Python, ML frameworks (TensorFlow/PyTorch/XGBoost), and cloud ML platforms (Azure ML, AWS SageMaker) - Familiarity with DCIM platforms (Schneider EcoStruxure IT, Nlyte, or Sunbird) desirable Note- Please apply via our official careers portal only, as applications sent directly to executives may not be considered.