Led the redesign of Cabify’s ML infrastructure, introducing KServe, Istio, and Knative to enable scalable, real-time model deployment and reduce infra usage by 50%.
Drove the platform selection process via technical evaluations, internal feedback sessions with 12 Data Scientists, and multiple proof-of-concept trials.
Developed real-time data connectivity for ML models by integrating ClickHouse into pipeline architecture, enabling responsive prediction for pricing and demand forecasting.
Designed and maintained high-throughput pipelines on GCP (GCS + BigQuery) using Scala and Apache Beam, processing 0.5+ TB/day and 7.5k+ events/sec.
Built and optimised Go-based ETLs to integrate third-party data into the Data Warehouse, improving ingestion performance and reliability.
Overhauled the Python-based machine learning platform to streamline model deployment, and operated nearly 20 live models with full MLOps responsibility.
Managed cloud infrastructure with Terraform, maintained Kubernetes clusters, and implemented automated deployments via ArgoCD.
Contained cloud spend growth despite a 4× increase in business volume, through infrastructure efficiency and proactive monitoring.
Led a 4-person Android team as Tech Lead, defining the technical roadmap and driving platform improvements for the Driver app.
Managed a cross-functional team of 7 (backend, frontend, and mobile), aligning product and engineering efforts for Driver-facing systems.
Played a key engineering leadership role during the integration of a major competitor, which doubled the number of active drivers (~50k) in key markets.
Collaborated closely with Product to ensure technical plans supported evolving business priorities and delivery timelines.
Actively supported career growth for team members through mentoring, role development, and performance coaching.
Led the rewrite of the Driver app from Java to Kotlin, designing a scalable architecture that supported a 50k-driver user base with bi-weekly releases and a 99.9% crash-free rate.
Contributed to the Rider app’s transition from Cordova to native Kotlin, helping define its RxJava-based architecture and developing native plugins. The app reached over 2 million downloads during this period.