ML Engineer
New
I
ITRex GroupLive-streaming Social Platforms
Belarus. Poland. Romania. Serbia. SpainFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English proficiency at an Upper-Intermediate level or above
- Experience
- 4+ years of experience as a Software Engineer, with at least 3 years in an ML Engineer role
- Required Skills
- AWSDockerPythonSQLGCPPyTorchNosqlTensorflowscikit-learnNLP
Requirements
- 4+ years of experience as a Software Engineer, with at least 3 years in an ML Engineer role
- Strong understanding of machine learning techniques including supervised & unsupervised learning, NLP, deep learning, and model evaluation
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy
- Hands-on experience in containerizing ML applications using Docker
- Practical experience with at least one cloud provider (AWS, GCP)
- Strong background in working with large datasets, SQL/NoSQL databases
- Expertise in debugging, optimizing, and enhancing models for performance, efficiency, and interpretability
- Experience maintaining ML workflows to ensure reproducibility, scalability, and operational efficiency
- Excellent communication skills
- Upper-Intermediate level or above in English
Responsibilities
- Design, develop, and deploy machine learning models for predictive analytics, classification, NLP, and other data-driven tasks
- Implement data pipelines for ingestion, preprocessing, feature engineering, and model training
- Containerize ML models and applications using Docker for scalable and reproducible deployments
- Deploy and maintain ML solutions in cloud environments (AWS/Snowflake)
- Optimize model performance, latency, and resource utilization for real-time or batch inference
- Monitor and troubleshoot ML models in production, ensuring reliability and robustness
- Collaborate with Product, Engineering, Data, and business stakeholders to define project requirements
- Conduct rigorous model evaluation using appropriate metrics to ensure performance and fairness
- Assess whether machine learning is necessary for a given problem or if alternative rule-based/statistical approaches are more appropriate
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