Applied AI Engineer for Fintech AI-Native Systems

Applied AI Engineer for Fintech AI-Native Systems

Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
Bjak

At a Glance

  • Tasks: Build AI-native systems for a Neobank app, turning ideas into production-ready workflows.
  • Company: Join BJAK, a forward-thinking fintech company revolutionising banking with AI.
  • Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
  • Other info: Dynamic team environment with a focus on collaboration and creativity.
  • Why this job: Make a real impact by enhancing customer experiences with cutting-edge AI technology.
  • Qualifications: Experience in AI development and a passion for innovative tech solutions.

The predicted salary is between 60000 - 80000 Β£ per year.

BJAK is seeking an applied AI engineer to build AI-native systems for its Neobank app in the UK. This is a hands-on role where you will turn AI ideas into production-ready workflows that automate real processes and improve products and customer experiences.

You will work with product and engineering teams to design, prototype and productionize AI-powered solutions, integrating LLMs, data, APIs and internal tools while ensuring reliability, safety and measurable business value.

Applied AI Engineer for Fintech AI-Native Systems employer: Bjak

BJAK is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for ambitious professionals looking to make a significant impact. With a focus on employee growth and development, we offer numerous opportunities for advancement in a fast-paced, high-growth environment. Join us in our mission to scale operations across multiple countries and be part of a team that values ownership and efficiency.

Bjak

Contact Details:

Bjak Recruitment Team

We think you need these skills to ace Applied AI Engineer for Fintech AI-Native Systems

Applied AI
AI-Native Systems
Workflow Automation
Product Development
Prototyping
Productionisation
Integration of LLMs