Salary: 20000-30000 PLN/month gross (80% KUP) negotiable
Location: Kraków, HYBRID 2days/home
Employment type: PERMANENT (Umowa o pracę)
As part of this role youll be designing, setting up and administering infrastructure for deploying, monitoring, and maintaining ML models.
Role and Responsibilities
- Create a machine learning infrastructure that is highly scalable and supports low latency and high throughput.
- Assist downstream applications with ML models, making sure they are safe, scalable, and obtainable.
- Oversee model iterations and guarantee that clients are provided with the most recent version. Establish a rollback procedure in the event that the current model version has problems.
- Use observability and monitoring technologies to keep tabs on the functionality, health, and usage of the platform and its constituent parts. Keep an eye on the functioning of the models that have been deployed, taking note of problems like concept drift, data drift, and model deterioration over time. Recognise problems early on and fix them to keep the system responsive and reliable..
Technologies in use
- Python
- REST
- Terraform
- TensorFlow
- PyTorch
- Github Actions
- Airflow
- Kubernetes
- Grafana
- AWS
- Spark
- Snowflake
- Snowpark
Competencies and Credentials
- degree in a relevant discipline, such as computer science.
- at least two years of validated microservices industry experience.
- familiarity with cloud solutions, orchestration tools (such as AWS, Sagemaker, Airflow, and AWS Step/Lambda), and infrastructure as code (Terraform).
- Possess knowledge of standard ML libraries, ETL, big data tools, and CI/CD (e.g., Docker, Kubernetes, TensorFlow, PyTorch, Spark ML, etc.), as well as MapReduce, Spark, Flink, Kafka, and Unix/Linux with shell.
- familiarity with real-time monitoring and alerting systems (like Prometheus and Grafana).
- familiarity with Go, Python, or other OOP languages.
- familiarity with distributed caching frameworks such as Redis/Aerospike.
Good to have
- at least five years of experience in the field of distributed microservices with high throughput, low latency, and integration, such as WS/REST.
- extensive background in machine learning system architecture design.
- Understanding of testing frameworks
