Lalit Wazir

Responsible to Design and develop low latency FX derivatives applications for financial institutions, hedge funds, and institutional investors. Collaborating with the core components team as a Full Stack Software Engineer to ensure the scalability and efficiency of the applications.

Led a master’s thesis on developing fast, accurate short-term availability forecasts for Mercedes-Benz EV charging stations. Achieved a 55.1% improvement in forecast accuracy by implementing two data processing pipelines using Pandas and Spark, optimizing for speed, accuracy, and cost. Utilized Python, TensorFlow, Keras, Pandas, Spark, and Azure technologies (Event Hubs, DevOps, Data Lake), deploying LSTM models on Databricks.

Part of the Charging Platform Team, collaborating with stakeholders to gather and refine requirements in RD/CUC. Improved data monitoring and visualization by developing a Kotlin-based Spring Boot application, delivering real-time charging station metrics from multiple sources via Datadog. Designed an automated anomaly detection system with a customizable rule-based data cleansing mechanism, ensuring efficient issue resolution and data integrity.

Contributed to Evolopro projects by streamlining development processes. Designed a Java-based code generation tool for OPC UA programs, utilizing Java, UML (PlantText UML), Eclipse Milo, Git, GitLab, and OPCUA. This solution significantly accelerated development cycles and improved machine-to-machine communication within a smart factory environment.

Worked with stakeholders to build solutions using SpringBoot, Java, Kotlin and SQL, ensuring timely delivery in Agile Scrum and providing Level 3 support. Developed and maintained features for two major Android banking apps, creating backend services and managing production issues for U.S. financial institutions. Improved web app response time by 30% with Tomcat load balancing. Enhanced security with SSL Pinning and IFrame bursting, and boosted customer satisfaction by implementing a profile update feature.