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Technical Lead - Data and AI
Accepting applicationsRiver · Bengaluru, Karnataka, India
Full-Time Mid_senior AIMachine LearningPythonSoCdeep learning
Estimated market salary
₹21-38 LPA
This is a SiliconBoard market estimate, not an employer-posted salary.
Posted
21h ago
Category
Manufacturing
Experience
Mid_senior
Country
India
About River
River is a design company building multi-utility products. Our flagship product River Indie #SUVofScooters, is designed to help you get things done. Engineered to be a dependable ally on your road to success.
We’re a 1000+ team headquartered in Bengaluru – backed by marquee international investors, with mobility-focused funds linked to Yamaha Motors, Al-Futtaim Automotive Group, Toyota VC, Trucks Venture Capital, Mitsui & Co. Ltd, Marubeni Ventures Inc., Lowercarbon Capital, and Maniv Mobility.
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
Modern 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
Show more Show less
River is a design company building multi-utility products. Our flagship product River Indie #SUVofScooters, is designed to help you get things done. Engineered to be a dependable ally on your road to success.
We’re a 1000+ team headquartered in Bengaluru – backed by marquee international investors, with mobility-focused funds linked to Yamaha Motors, Al-Futtaim Automotive Group, Toyota VC, Trucks Venture Capital, Mitsui & Co. Ltd, Marubeni Ventures Inc., Lowercarbon Capital, and Maniv Mobility.
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
Modern 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
Show more Show less