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WHAT YOU’LL DO
Work on development and conceptual tasks along the entire AI product lifecycle as well as on technology-related enablement of AI Platform users within an agile team.
Contribute to infrastructure maintenance and to building reusable enterprise-grade software components required for fast delivery of productive ML models, increased process automation and accelerated adoption of MLOps best practices across the AI lifecycle.
Support in ML engineering, ML operations, Data Science tasks and maintenance of models including retraining and model optimizations with a solid Data Science and Data Engineering skillset.
Take over technical responsibility for individual software components, data science models and ETL pipelines.
Support our AI/ML architectures for sustainable production readiness.
Automate AI infrastructures for the data science team.
Contribute to team’s winning plan, present success story and contribute to the creation of product-related non-code-assets (such as rollout and knowledge transfer material).
Educate customers, partners and stakeholders about AI/ML.
Continuous learning and familiarization with state-of-the-art algorithms and technologies in agile software engineering, MLOps and AI.
WHAT YOU’LL BRING
Bachelor’s or master’s degree in computer science, data science, engineering or related technical fields.
At least 2+ years work experience in data science, data engineering and software development.
Solid understanding of AI/ML concepts including Deep Learning, GenA, MLOps and AI lifecycle.
Solid understanding of a variety of Python libraries and Machine Learning frameworks such as Numpy, Pandas, Keras, scikit-learn, TensorFlow, PyTorch.
Experience in Python backend development such as FastAPI, Postgres, OpenAPI.
Experience in DevOps practices and tools such as Jenkins, GitHub, XMake.
Experience with development tools such as Jupyter, Docker, Kubernetes, Terraform, Github and major (ML) programming languages such as Python, Java etc.
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