Software Engineer (MLOps)

Tasks:
- Designing, preparing, and developing an environment in AWS Cloud and Bare Metal servers simultaneously;
- Developing scalable and reliable ML infrastructure components to facilitate the deployment of ML models;
- Making ML models available in the form of APIs;
- Operationalizing models on AWS infrastructure;
- Creating scalable automated processes for the large-scale deployment of ML models;
- Developing systems to monitor the ML model performance.

Must-haves:
- Experience in deploying ML APIs/containers – such as Docker, Kubernetes, or OpenShift – to the public cloud (Azure, AWS, or Google Cloud) and ML lifecycle platforms (MLflow, Kubeflow, Apache Airflow) for 1+ years;
- Experience in designing data processing pipelines using Kafka, Elasticsearch, and Kibana;
- Knowledge of any tool for logging and monitoring, such as Grafana, Prometheus, Splunk, etc.;
- Strong software engineering skills and experience with Python;
- Level of English – Intermediate.