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engineer
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Forecasting & Anomaly Detection: Driving insights across business metrics, supply & demand (e.g., ride-hailing), renewable energy, weather, and internal system/database anomalies.
Data Systems & Engineering: Designing and implementing OLAP, real-time streaming, and batch processing solutions.
MLOps & Production ML: Building robust ML pipelines, online inference systems, and microservices architectures for production.
Scalable & High-Performance Computing: Expertise in GPU parallelization and HPC systems design.
Numerical Methods & Optimization: Proficiency in optimization, adjoint methods, automatic differentiation, and numerical solvers (fluid dynamics, structural mechanics).
Reliability & Operations: Ensuring system stability through incident response and proactive monitoring.
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Lead Machine Learning Engineer at Grab
— previously—Research Engineer at CFD-Berlin
Engineer Research at GreenStone Energy
Marie Curie Early Stage Researcher at von Karman Institute for Fluid Dynamics
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Programming: Python, Go, Scala, C/C++
ML Frameworks: PyTorch, TensorFlow, scikit-learn
Big Data & Streaming: Spark, Flink, Kafka, Airflow, Pinot
Cloud & MLOps: AWS, Google Cloud, Kubernetes, Docker, CI/CD, Git
Distributed Systems: Ray, OpenMP, MPI, CUDA/OpenCL, Slurm, REST, gRPC
Data Stores: SQL (MySQL/TimescaleDB), Redis, S3
Monitoring & Observability: Datadog, Grafana, PagerDuty, Splunk
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Dr.-Ing. in Computer Science, magna cum laude, from TU Kaiserslautern / von Karman Institute for Fluid Dynamics
M.Sc. in Computational Engineering Science, mit Auszeichnung, from RWTH Aachen
B.Sc. in Computational Engineering Science from RWTH Aachen