Data Engineer

The Opportunity

We are a dedicated team of six engineers developing flagship AI and ML projects that are transforming the Credit and Lending landscape. In our fast-paced, innovative, and stable environment, you will collaborate closely with business stakeholders to deliver high impact solutions. If you are a leader who is passionate about applying cutting-edge AI to solve real-world financial challenges, we want to hear from you.

What you’ll do

• Write and maintain exemplary, high-quality, testable, and reusable code.

• Lead the design of AI/ML solutions, ensuring they are scalable, robust, and aligned with the platform’s vision.

• Own the engineering execution for significant parts of our AI flagship projects, from technical design through to production deployment.

• Mentor and guide other engineers on the team, growing a culture of technical excellence and continuous improvement.

• Co-author and champion coding standards and AI best practices to ensure fairness, transparency, and accountability across our models.

• Co-drive technical strategy by evaluating and prototyping new technologies, such as advanced RAG techniques, LLM optimizers, and agentic architectures.

• Collaborate extensively with stakeholders across the AI Programme, translating business requirements into technical solutions and communicating complex ideas to diverse audiences.

• Develop and implement sophisticated solutions using Retrieval-Augmented Generation (RAG) and vector databases to unlock insights from complex financial data.

Requirements

• Demonstrated experience in a senior engineering role, with a track record of leading technical projects and mentoring others.

• Deep expertise in Python and its ML/AI ecosystem. Familiarity with Java for occasional middleware and integration tasks is a plus.

• Relational databases (PostgreSQL, etc.), Vector Databases, Key-Value stores (Redis, etc.).

• A strong command of microservices architecture, observability, API design, and concurrency models, with experience in making architectural decisions.

• Expert-level understanding of LLM architectures and extensive experience fine-tuning models on domain-specific data.

• Hands-on mastery of Retrieval-Augmented Generation (RAG), advanced prompt engineering, and designing agentic architectures.

• In-depth knowledge of machine learning, deep learning, and NLP, with proven experience leading the development and experimentation of LLMs.

• Proficiency with Azure or GCP, containers, Kubernetes, and CI/CD tools (e.g., Jenkins, Azure DevOps, GCP Cloud Build).

• Practical experience in TDD and BDD.