About Snappi
At Snappi, we're building a neobank from the ground up. Our mission is to empower financial freedom through technology, offering innovative and transparent digital banking solutions.
Join us in reshaping the financial landscape!
About the Role
We are seeking a Machine Learning Ops Engineer to join our dynamic team. In this role, you will be responsible for deploying, managing, and maintaining machine learning models in production environments, ensuring they operate at peak performance while continuously improving overall efficiency. With a focus on automation, compliance, and performance, the role ensures that data-driven products—such as credit risk engines, fraud detection algorithms, and personalization systems—operate consistently in production environments. Collaboration with data scientists, infrastructure teams, and risk and compliance units is central to this role, which sits at the core of Snappi’s AI-driven strategy for digital financial services.
Key Responsibilities
- 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 to ensure full traceability and compliance.
- Collaborate with Data Science, Engineering, and Product teams to streamline model handoff and delivery.
- 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 in line with GDPR and PSD2.
- Develop observability layers (e.g., Prometheus, Grafana, ELK) with auto-alerting for SLA violations.
- Benchmark and reduce infrastructure and prediction costs through profiling and resource right-sizing.
- Contribute to a culture of experimentation, automation, and blameless post-mortems
Requirements
- 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 (Terraform, Helm).
- Familiar with monitoring, alerting, and log aggregation platforms.
- Strong communication skills with ability to document and present to both technical and non-technical stakeholders.
- Prior experience in finance or other regulated industries is a strong plus.
Benefits
Our benefits are thoughtfully designed not only with your well-being in mind but also to support your overall professional and personal growth. They include:
- Competitive salary, aligned with your experience and skills.
- Hybrid work flexibility.
- 37-hour work week.
- Additional paid days off: Enjoy an extra day off for every five days taken between January to April and October to December.
- Medical & Life insurance.
- Employer-sponsored pension plan.
- Dedicated savings plan for your children.
- Daycare allowance, helping you cover preschool costs.
- Additional School Monitoring Days: 6 Days for your first child, plus 4 days for each additional child.
- Special rates on our banking products.
- Continuous learning opportunities & career advancement support.
We are an equal opportunity employer and values diversity. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status or disability status.