Responsibilities

What You Will Own
Data Platform (Core)
Architect and scale a modern data lake supporting both batch and real-time (streaming) workloads
Design and manage reliable data ingestion pipelines
Data modeling & governance
Data quality & lineage
Build and maintain a scalable feature store for ML and AI systems
Develop a robust semantic layer that enables analytics, experimentation, and AI use cases
ML & Data Science
Own the complete ML lifecycle — from training and validation to deployment, monitoring, and retraining
Build and operationalize predictive, and optimization models
Establish experimentation frameworks and model evaluation standards
Implement drift detection, monitoring, and performance tracking to ensure model reliability
AI Readiness
Design and deploy RAG (Retrieval-Augmented Generation) pipelines
Build and manage embeddings infrastructure
Structure and maintain AI-ready datasets optimized for LLMs and agent-based systems
Implement feedback loops that enable continuous learning and system improvement
What You’ll Build
Central data lake (events, operational, external)
Real-time pipelines (Kafka / PubSub / streaming)
ML pipelines (forecasting, anomaly detection, optimization)
Feature store
Experiment tracking
Data observability
Decision intelligence dashboards
AI-ready datasets
How You’ll Work
Architecture First
Define the complete data architecture: Source → Ingestion → Storage → Transformation → Serving
Design and optimize offline vs real-time processing strategies
Make informed cost vs performance trade-offs
ML in Production
Model deployment pipelines
Versioning
Monitoring
Retraining strategies
Cross-Functional
Work with Product → define signals
Work with Engineering → integrate pipelines
Work with AI teams → enable agent intelligence