Machine Learning Ops Engineer
Job Description
Snappi, an innovative neobank, is revolutionizing digital banking with a focus on technology-driven financial freedom. We're seeking a Machine Learning Ops Engineer to join our dynamic team in Athens, Greece. This role is crucial for deploying and maintaining ML models in production environments, particularly for credit risk engines, fraud detection, and personalization systems.
The position requires a blend of DevOps expertise and machine learning knowledge, with responsibilities spanning from architecting ML infrastructure to ensuring regulatory compliance. You'll work with cutting-edge technologies including Kubernetes, Docker, and cloud platforms, while collaborating across teams to deliver robust ML solutions.
The ideal candidate brings 3+ years of DevOps/SRE experience, with 1-2 years specifically in MLOps. Strong Python skills are essential, and experience with Scala, Go, or Rust is valuable. Knowledge of ML pipeline tools and regulatory frameworks in finance is crucial.
We offer a compelling benefits package including medical insurance, pension plan, and family-friendly policies like daycare allowance and school monitoring days. The hybrid work environment and 37-hour work week promote work-life balance, while competitive compensation and banking perks add additional value.
Join us in shaping the future of digital banking while working with cutting-edge ML technologies in a supportive, growth-oriented environment.
Responsibilities For Machine Learning Ops Engineer
- Architect and maintain production-grade ML infrastructure across cloud platforms
- Build robust CI/CD/CT pipelines for model training, testing, validation, and deployment
- Integrate with model governance, risk, and audit systems
- Collaborate with Data Science, Engineering, and Product teams
- Automate model monitoring for drift, latency, fairness, and performance degradation
- Optimize and containerize ML workflows using Kubernetes, Docker, and orchestration tools
- Ensure data privacy, encryption, and secure access to ML artifacts
- Develop observability layers with auto-alerting for SLA violations
- Benchmark and reduce infrastructure and prediction costs
- Contribute to a culture of experimentation, automation, and blameless post-mortems
Requirements For Machine Learning Ops Engineer
- Bachelor's or Master's in Computer Science, Data Engineering, or related field
- 3+ years in DevOps/SRE roles; 1-2 years focused on MLOps in production settings
- Proficient in Python; bonus for Scala, Go, or Rust experience
- Hands-on with Kubernetes, Docker, and at least one major cloud (GCP, AWS, or Azure)
- Experience with ML pipeline tools (Kubeflow, MLflow, TFX, or Airflow)
- Understanding of model risk management, regulatory frameworks, and data lineage
- Comfortable with version control (Git), GitOps practices, and infra-as-code
- Familiar with monitoring, alerting, and log aggregation platforms
- Strong communication skills
- Prior experience in finance or other regulated industries is a strong plus
Benefits For Machine Learning Ops Engineer
- Competitive salary
- Hybrid work flexibility
- 37-hour work week
- Additional paid days off
- Medical & Life insurance
- Employer-sponsored pension plan
- Dedicated savings plan for children
- Daycare allowance
- Additional School Monitoring Days
- Special rates on banking products
- Continuous learning opportunities & career advancement support
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