Key Responsibilities
Leadership and 0-to-1 Strategy
Team Building: Recruit, mentor, and manage a hybrid team of data engineers,
data scientists, and ML engineers from scratch
Unified Roadmap: Define and execute a multi-year technical vision that
bridges scalable data infrastructure with advanced AI capabilities
Cross-Functional Impact: Partner with Vehicle Engineering, Software,
Manufacturing, and Sales to identify high-impact AI/Data use cases (e.g.,
supply chain forecasting, smart scooter features)
Data Engineering & Infrastructure
IoT Telemetry Pipelines: Architect low-latency, high-throughput streaming
pipelines to ingest real-time data from vehicle sensors (VCU, BMS), mobile
apps, and charging infrastructure using MQTT, Apache Kafka, or AWS Kinesis
Up-to-date Data Stack: Design and scale a unified Lakehouse/Warehouse (e.g.,
Databricks, Snowflake) to handle both streaming telemetry and complex
enterprise data (ERP, CRM, MES)
Data Pipelines: Build automated, resilient ETL/ELT workflows using tools
like Apache Airflow, dbt,



and PySpark to ensure high data quality and governance
Artificial Intelligence & Machine Learning
Predictive Modeling: Develop and deploy ML models specific to the EV
ecosystem—such as predictive maintenance, Battery State of Health (SoH)
degradation forecasting, and energetic Range/State of Charge (SoC) estimation
Rider Intelligence: Build algorithms to analyze rider behavior, detect
anomalies (e.g., accident or fall detection), and personalize the app/scooter
experience
MLOps & Deployment: Establish the MLOps infrastructure (e.g., MLflow,
Kubeflow) to train, deploy, monitor, and retrain models seamlessly in
production—both in the cloud and on edge devices (vehicle ECUs)

Ideal Candidate
Experience: 8–12+ years of comprehensive experience across Data Engineering
and Data Science, with at least 2–3 years leading technical teams or complex,
multi-disciplinary data projects
Domain Expertise: Prior experience in Automotive,

📌 Technical Lead Bengaluru
🏢 Business Applications
📍 Bengaluru