Farhan Dilawari

• Designed and deployed production-grade machine learning models for predictive maintenance and energy optimization. • Built end-to-end ML pipelines, including data ingestion, model training, and real-time inference systems. • Led AI initiatives using deep learning frameworks (TensorFlow/PyTorch) and implemented MLOps best practices.

• Built and managed large-scale data pipelines (ETL) for business intelligence and analytics use cases. • Worked with big data technologies (Hadoop/Spark) to process and analyze high-volume retail data. • Partnered with business teams to deliver data-driven insights for pricing, supply chain, and customer behavior.

• Led backend development for enterprise systems, including APIs, inventory platforms, and demand forecasting tools. • Designed scalable system architectures and improved performance of high-traffic applications. • Mentored junior engineers and contributed to system design decisions and technical leadership.

• Designed and optimized backend systems for large-scale data collection and processing using relational databases. • Worked on data-intensive applications supporting national surveys, improving data accuracy and processing efficiency. • Collaborated on scalable system components, focusing on performance tuning and reliability.

• Developed core backend modules using C/C++ and Java, contributing to early-stage financial systems and internal tools. • Built and maintained SQL-based data processing scripts for risk and transaction analysis. • Gained strong foundations in software engineering, debugging, and structured programming practices.