Junior ML Ops Engineer
As our portfolio of production AI systems continues to grow, so does the need for engineering excellence around reliability, scalability, and governance. To sustain this momentum, we are strengthening the team that ensures our models run seamlessly in production—delivering value every day while meeting the highest standards of compliance and observability.
The Data Science team is looking for a Junior MLOps Engineer to help improve and streamline the lifecycle of our AI models, from development to production. This role is particularly exciting because you will not be limited to a single domain; you will work across a diverse array of topics surrounding production-deployed models, gaining deep hands-on experience with the entire lifecycle of an AI product.
By joining the team, you will contribute to building the backbone that supports the bank's AI capabilities, ensuring efficiency and governance across the board.
As a Junior MLOps Engineer You Will:
- Automate Monitoring & Observability: Develop systems to automate the evaluation of deployed AI applications. You will work on improving observability for both classical Machine Learning models and Generative Models, ensuring we have real-time insights into performance and health.
- Build Deployment Platforms: Contribute to the development of an internal platform based on MLflow. You will help streamline the developer experience by creating tools for model and agent versioning, packaging, and seamless deployment onto our internal infrastructure.
- Orchestrate Pipelines: Design, optimize, and orchestrate complex data and training pipelines using Argo Workflows, ensuring that our model training processes are reproducible and efficient.
- Enforce AI Governance: Help build a governance platform that acts as a central control plane, ensuring that all deployed AI applications strictly adhere to company regulations and compliance standards.
Qualifications
- Educational Background: Degree in Computer Science, Data Science, Engineering, or a related field.
- Python Proficiency: Strong command of Python; you write clean, maintainable code and care about engineering best practices.
- MLOps/DevOps Foundations: Understanding of DevOps concepts such as CI/CD (GitHub Actions) and containerization (Docker, Kubernetes). Prior hands-on experience is a strong plus.
- Infrastructure Mindset: You are motivated by "what happens after the model is trained"—specifically how models are deployed, scaled, and monitored in production environments.
- Web Development Awareness: Familiarity with frontend development is a plus (for building internal tools and dashboards).
- Growth Mindset: You are organized, self-motivated, and comfortable ramping up quickly on new technologies and tools.
- Communication: Fluent in English and able to collaborate effectively within a technical team.