🛠 Tech Stack

💼 About This Role

You'll build ML infrastructure from scratch for a Risk team, creating automated training pipelines and a unified feature platform. Your work will directly impact real-time production inference with strict SLAs. This role offers the chance to design and own the entire ML lifecycle at a high-tech fintech company.

🎯 What You'll Do

• Evaluate and recommend Feature Store solutions (Feast, Tecton, or custom)
• Design and build ML training and deployment pipelines
• Set up monitoring of model quality and feature health
• Define alerting and response processes with DS team

📋 Requirements

• 3+ years in ML Engineering, Data Engineering, or DevOps
• Experience building ML training pipelines with experiment tracking and model registry
• Understanding of feature store concepts and train-serve consistency
• Strong Python skills and experience with Docker and ML pipeline orchestration

✨ Nice to Have

• Experience with Kubeflow, Argo Workflows, or Metaflow
• Solid SQL and data warehouse architecture knowledge
• B1+ English level

🎁 Benefits & Perks

• 🏠 Relocation support to hubs in Cyprus, Serbia, Georgia, or Kazakhstan
• 💻 Flexible work - remote or office
• 📚 Education budget for language lessons, training, certifications
• 🧘 Wellness budget for mental health and fitness
• 🏖️ 20 days annual leave and paid sick leave

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Call· 30 min
2. 2Technical Interview· 60 min
3. 3System Design Interview· 60 min
4. 4Hiring Manager Interview· 45 min