{"id":1266392,"url":"https://alion.io/job/river-mobility-private-limited-technical-lead","title":"Technical Lead","company":{"id":2174120,"name":"River Mobility Private Limited","domain":"rideriver.com","url":"https://alion.io/company/rideriver","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":59000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":51},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Flink","optional":false},{"name":"Kubeflow","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"MQTT","optional":false},{"name":"Pandas","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-09-04T12:07:34Z","employer_posted_date":null,"last_verified_at":"2026-09-04T12:07:34Z","board_verified":false,"closed_at":null,"days_open":25,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":25},"description":"About River : \n\nRiver 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.\n\nKey Responsibilities : \n\nLeadership and 0-to-1 Strategy : \n\n- Team Building : Recruit, mentor, and manage a hybrid team of data engineers, data scientists, and ML engineers from scratch.\n\n- Unified Roadmap : Define and execute a multi-year technical vision that bridges scalable data infrastructure with advanced AI capabilities.\n\n- 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).\n\nData Engineering & Infrastructure : \n\n- 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.\n\n- 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).\n\n- Data Pipelines : Build automated, resilient ETL/ELT workflows using tools like Apache Airflow, dbt, and PySpark to ensure high data quality and governance.\n\nArtificial Intelligence & Machine Learning : \n\n- 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.\n\n- Rider Intelligence : Build algorithms to analyze rider behavior, detect anomalies (e.g., accident or fall detection), and personalize the app/scooter experience.\n\n- 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).\n\nIdeal Candidate : \n\n- Experience : 8 - 10+ 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.\n\n- 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.\n\n- Languages : Expert-level proficiency in Python and SQL.\n\n- Data Engineering : Deep practical experience with stream processing (Kafka, Spark Streaming, Flink), cloud data warehouses, and orchestration (Airflow, dbt).\n\n- AI/ML Frameworks : Strong hands-on experience with machine learning libraries (Scikit-learn, XGBoost, Pandas) and deep learning frameworks (PyTorch or TensorFlow).\n\n- MLOps : Proven track record of deploying machine learning models into live production environments and monitoring model drift.\n\n- 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.\n\n- Mandatory 5 days work from office.\nSkills\nData Engineering, Data Infrastructure, Kafka, MQTT Protocols, Data Pipeline, ETL, Apache Airflow, MLOps, Machine Learning, Artificial Intelligence, Python","description_format":"text","description_chars":3622,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Transportation & Logistics","Shared Mobility","Smart Mobility"],"lifecycle":[{"event":"open","at":"2026-09-25T22:00:00Z"}],"liveness":{"score":31,"band":"fade","label":"Fading","p_open":0.85,"p_active":0.655,"p_room":0.55,"age_days":24,"expected_fill_days":24,"reasons":["seen:24","win:tail"],"computed_at":"2026-09-29T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/river-mobility-private-limited-technical-lead","json_url":"https://alion.io/job/river-mobility-private-limited-technical-lead.json","meta":{"generated_at":"2026-09-30T03:01:15Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2325,"day_limit":5000,"remaining_today":2675,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}