Minimum 3 years of professional experience in Software Engineering or Machine Learning Engineering.
Strong Python skills, including testing, packaging, API development, and code review.
Practical experience deploying and operating Machine Learning models in production environments.
Experience with CI/CD pipelines, model registration, and lifecycle management using MLflow or similar platforms.
Experience building reproducible ML training workflows and managing model deployment, monitoring, retraining, and rollback procedures.
Experience with batch and real-time model serving.
Knowledge of containers and distributed processing using Spark.
Experience implementing model monitoring, data and prediction drift detection, alerting, and Service Level Objectives (SLOs).
Working knowledge of Microsoft Azure and Infrastructure as Code concepts.
Understanding of the end-to-end Machine Learning lifecycle, from experimentation to production deployment.
Experience developing APIs and integrating ML models into business applications and operational systems.
Knowledge of model evaluation metrics, production monitoring, and performance optimization.
Ability to refactor data science code into maintainable, tested production services and reusable libraries.
Strong understanding of MLOps practices, production readiness, incident response, and model lifecycle management.
Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
Fluent English (mandatory).
Experience with real-time decisioning in payments, fintech, fraud detection, or credit-related environments.
Experience operating LLM-based applications in production, including LLMOps, AgentOps, model evaluation, tracing, prompt versioning, and AI guardrails.
Experience with Feature Stores and continuous-training pipelines.
Knowledge of event-driven architectures using Kafka and low-latency inference solutions.
Experience with Azure ML, Terraform, and container-based serving platforms.
Understanding of model risk management and EU AI Act requirements.
Master's degree in Computer Science, Machine Learning, or a related field.
Azure, Databricks, or Machine Learning Engineering certifications.
Special discounts and flex options;
Training annual budget;
A great team-oriented culture;
Chance to become a part of a dynamically growing international company;
Challenging projects giving the unique opportunity to grow and learn.
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