Senior mlops engineer [up to 25k]

about the company

our client specializes in transforming marketing actions into measurable outcomes by leveraging advanced capabilities in optimization, analytics, artificial intelligence, and experimentation.

• monitor and maintain ML models in production to ensure performance and reliability
• implement automated retraining and fine-tuning workflows based on model degradation
• support scalable deployment of models across multiple environments and regions
• troubleshoot production ML issues and optimize system reliability and latency
• design and implement automated ML pipelines for training, validation, and deployment

• hands-on experience building scalable ML infrastructure and big data systems
• solid background using MLOps tools such as Airflow and MLflow
• proven track record deploying and scaling ML models in production environments
• developed high-volume model inference pipelines with Spark or Databricks
• experienced with core ML libraries including scikit-learn, PyTorch, and Tensorflow
• have experience with the end-to-end Machine Learning SDLC

how to apply

• experience

• skills

• qualifications

• education

Bachelor Degree

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