Dimitris Poulopoulos
Senior Machine Learning Engineer at Mozilla.ai
Athens, Attica, Greece
Summary
🤩
Rockstar🎓
Top SchoolDimitris Poulopoulos is a Senior Machine Learning and MLOps Engineer with 11 years of experience designing and scaling Kubernetes-native AI platforms and production ML workflows. He combines deep expertise in distributed training, LLM fine-tuning (PEFT, RLHF, DPO), and model optimization with hands-on automation of Kubernetes deployments and CI/CD for hybrid and multi-cloud environments. An active Kubeflow contributor and former core developer on the widely used Kale project, he bridges data scientist ergonomics and production reliability by implementing model serialization and multi-framework support (TF, PyTorch, XGBoost). At Mozilla.ai he helps make open-source LLM evaluation tooling mainstream, building on prior roles at HPE and Arrikto where he integrated NVIDIA inference stacks, RAG systems, and vector DBs. He mentors engineers, holds advanced research experience from a PhD-track role, and is known for translating research-grade ML into robust, maintainable infrastructure.
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD (Not completed), Data Science Lab, Doctor of Philosophy - PhD (Not completed), Data Science Lab at University of Piraeus
Greek, English, French
Stackoverflow
1,159reputation
115kreached
1answer
43questions
Github Skills (21)
serializer10
python10
kubeflow10
machine-learning10
serialization10
tensorflow10
jupyter-notebook10
kubeflow-pipelines10
back-end-development9
pytorch9
backend9
xgboost8
sql-server6
amazon-sagemaker6
android-library6
Programming languages (8)
Github contributions (5)
kubeflow/kale
Sep 2020 - Nov 2020
Kubeflow’s superfood for Data Scientists
Role in this project:
ML Engineer & Backend Developer
ML Engineer & Backend DeveloperContributions:4 reviews, 9 commits, 12 PRs in 2 months
Contributions summary:Dimitris contributed to the `kale` repository, focused on streamlining the Kubeflow pipeline development experience for data scientists. Their work involved implementing TensorFlow model serialization using the SavedModel format, enhancing the marshalling backend to support TF Keras models. They also added support for TensorFlow 2.x in the example notebooks and fixed issues with the dog breed dataset. Furthermore, they included support for XGBoost and PyTorch models.
kubeflowkubeflow-pipelinesjupyter-notebookmachine-learning
HPEEzmeral/AI-Demos
May 2023 - Oct 2024
Ezmeral Unified Analytics Software - Tutorials and Demos.
Contributions:5 reviews, 39 PRs, 359 pushes in 1 year 4 months
ezmeral
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