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 dynamic 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, EV, Telematics, or IoT is highly preferred. You must be comfortable dealing with high-frequency time-series data and geospatial (GPS) data Languages: Expert-level proficiency in Python and SQL Data Engineering: Deep practical experience with stream processing (Kafka, Spark Streaming, Flink), cloud data warehouses, and orchestration (Airflow, dbt) AI/ML Frameworks: Strong hands-on experience with machine learning libraries (Scikit-learn, XGBoost, Pandas) and deep learning frameworks (PyTorch or TensorFlow) MLOps: Proven track record of deploying machine learning models into live production environments and monitoring model drift Player-Coach Mentality: You possess the strategic vision to design enterprise architecture, but you still love writing production-grade code, debugging PySpark jobs, and tuning neural networks Mandatory 5 days work from office

📌 Technical Lead - Data and AI (Bengaluru)
🏢 Business Applications
📍 Bengaluru