05.01.2025 aktualisiert
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Machine Learning | NLP | Full-Stack Engineer
Berlin, Deutschland
Weltweit
Master of Science (M.Sc.)Skills
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My expertise is in the design and development of data-intensive applications that use Machine Learning or NLP to solve problems in various domains.
- Fields: Machine Learning, Natural Language Processing, Information Retrieval, Recommender Systems
- ML/NLP: Keras, pyTorch, spaCy, Pandas, scikit-learn, gensim, numpy, Jupyter, matplotlib
- Backend: Java & Kotlin with Spring Boot, Python with Flask or FastAPI
- Databases: NoSQL (Elasticsearch, MongoDB, Neo4j), SQL (PostgreSQL)
- DevOps & Cloud: CI (Jenkins, Drone), Kubernetes, Docker, AWS (SageMaker, SQS, ECS, S3, Lambda, API Gateway, DynamoDB, RDS, Cloudwatch)
- Workflow Engines: Apache Airflow, Argo Workflows
- Frontend: JavaScript, Typescript, material-ui, React
- Others: Project Management, Technical Lead, Scrum, Kanban
My expertise is in the design and development of data-intensive applications that use Machine Learning or NLP to solve problems in various domains.
- Fields: Machine Learning, Natural Language Processing, Information Retrieval, Recommender Systems
- ML/NLP: Keras, pyTorch, spaCy, Pandas, scikit-learn, gensim, numpy, Jupyter, matplotlib
- Backend: Java & Kotlin with Spring Boot, Python with Flask or FastAPI
- Databases: NoSQL (Elasticsearch, MongoDB, Neo4j), SQL (PostgreSQL)
- DevOps & Cloud: CI (Jenkins, Drone), Kubernetes, Docker, AWS (SageMaker, SQS, ECS, S3, Lambda, API Gateway, DynamoDB, RDS, Cloudwatch)
- Workflow Engines: Apache Airflow, Argo Workflows
- Frontend: JavaScript, Typescript, material-ui, React
- Others: Project Management, Technical Lead, Scrum, Kanban
Sprachen
DeutschMutterspracheEnglischverhandlungssicher
Projekthistorie
Description
I build and evaluated machine learning models to predict product characteristics. One of my notable achievements includes developing a multi-output neural network that was successfully deployed in production, enriching millions of products. In addition, I extended and created several streaming services (stateful and stateless) and contributed to their functionality and scalability, allowing millions of products and predictions to be processed efficiently.
Summary
I build and evaluated machine learning models to predict product characteristics. One of my notable achievements includes developing a multi-output neural network that was successfully deployed in production, enriching millions of products. In addition, I extended and created several streaming services (stateful and stateless) and contributed to their functionality and scalability, allowing millions of products and predictions to be processed efficiently.
Summary
- Technology: pyTorch, Keras, Python, Java, Spring Boot, JPA, Kafka, AWS (SageMaker, PostgreSQL,
S3, Cloudformation, Cloudwatch) - Prototyping & evaluation of various ML models
- Development of a multi-output neural network that was successfully deployed in production, en- riching millions of products
- DB design and optimization for PostgreSQL databases
- Optimization of Kafka streaming infrastructure to keep up with growing amount of data to be processed
- Cost analysis & reduction of AWS services
- Improvements and extension of backend services
Description
Collaboration on various projects in the neuroscience environment. Technologically, the applications in these projects often consisted of a Python backend and a React frontend deployed in a Kubernetes cluster.
Summary
Collaboration on various projects in the neuroscience environment. Technologically, the applications in these projects often consisted of a Python backend and a React frontend deployed in a Kubernetes cluster.
Summary
- Technology: Python, React, JavaScript, material-ui, Three.js, Kubernetes, Jupyter Hub, CodeFresh (CI)
- Besides product development also responsible for project management and sprint organization
- Building a deployment pipeline in Codefresh and configuration of the Helm Chart and Kubernetes Files
- Designed and implemented a feature to enable parallel simulations, consisting of UI views to con- figure simulations and backend logic to manage and execute simulations in multiple processes
- Developing new React Components and extending Redux Actions and Reducers to reflect a new UI flow
- Development of end-to-end tests using Jest and Puppeteer together with integration of the tests into the existing deployment pipeline