Back to SearchSenior Machine Learning Engineer
Hybrid in Hungary
Machine Learning Engineering
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We are seeking an experienced Senior Machine Learning Engineer to join our team. The ideal candidate will take on leading roles in designing, developing, and optimizing our machine-learning platform. Your contributions will drive the success of our prediction models in real-world applications.
Responsibilities
- Contribute to the design, development, and operational lifecycle of the ML pipeline based on best practices
- Design, create, maintain, troubleshoot, and optimize ML pipeline steps
- Own and contribute to the design and implementation of ML prediction endpoints
- Collaborate with System Engineers to configure the ML lifecycle management environment
- Write specifications, documentation, and user guides for developed applications
- Promote improved coding practices and repository organization in the science work cycle
- Establish and configure pipelines for projects
- Identify technical risks and gaps, and devise mitigation strategies
- Collaborate with data scientists to productionalize predictive models, understand the scope and purpose of the models built by data scientists, and create scalable data preparation pipelines
Requirements
- Minimum of 3 years programming language experience, ideally in Python, and strong SQL knowledge
- Robust MLOps experience (Sagemaker, Vertex, or Azure ML)
- Intermediate level in Data Science, Data Engineering, and DevOps Engineering
- Experience with at least one project delivered to production in an MLE role
- Expertise in Engineering Best Practices
- Practical experience in implementing Data Products using the Apache Spark Ecosystem (Spark SQL, MLlib/SparkML) or alternative technologies
- Experience with Big Data technologies (e.g., Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc.)
- Proficiency in automated data pipeline and workflow management tools, i.e., Airflow, Argo Workflow, etc
- Experience in different data processing paradigms (batch, micro-batch, streaming)
- Practical experience working with at least one major Cloud Provider such as AWS, GCP, and Azure
- Production experience in integrating ML models into complex data-driven systems
- DS experience with Tensorflow/PyTorch/XGBoost, NumPy, SciPy, Scikit-learn, Pandas, Keras, Spacy, HuggingFace, Transformers
- Experience with different types of databases (Relational, NoSQL, Graph, Document, Columnar, Time Series, etc.)