Role overview
Our AI systems are already delivering measurable impact across the bank, from classical ML models powering core business processes to a growing suite of LLM-powered applications transforming how teams work. As we scale this portfolio, we need engineers who can push the boundaries of what's possible while keeping production systems robust and performant.
The Data Science team is looking for a Junior Machine Learning Engineer to design, build, and ship AI-powered solutions — with a strong emphasis on Large Language Model applications. You will be involved in projects end-to-end: from prototyping and experimentation through to production-grade systems serving real users. This is a high-impact role where you will take on real responsibility early and shape how AI is built and delivered across the organization.
As a Junior Machine Learning Engineer You Will:
- Build LLM-Powered Applications: Design and develop applications leveraging Large Language Models — including RAG systems, agentic workflows, and conversational interfaces — tailored to complex business needs in a regulated environment.
- Develop & Fine-Tune Models: Train, fine-tune, and evaluate both classical ML and language models, selecting the right approach for each problem and optimizing for production constraints such as latency, cost, and accuracy.
- Engineer for Production: Build reliable, scalable ML services and APIs. You care about code quality, testing, and maintainability as much as model performance.
- Evaluate & Iterate: Contribute to evaluation frameworks for AI applications — particularly for generative systems where traditional metrics fall short — and use them to drive continuous improvement.
- Stay on the Cutting Edge: Actively track the fast-moving LLM landscape, assess emerging tools and techniques (new model releases, orchestration frameworks, prompting strategies), and translate them into practical value for the team.
- Collaborate Across Teams: Work closely with data scientists and MLOps engineers, product owners, and business stakeholders to translate business problems into well-scoped AI solutions.