AI Engineer – Machine Learning & Edge AI (Computer Vision)

About the Role

We’re building the brains behind straight-through-processing (STP) in banking - AI models that can recognize, evaluate, and validate information in real time on edge devices. As an AI Engineer, you’ll join our R&D team to develop and refine computer vision models for edge systems, combining deep learning expertise with hands-on software development.

You’ll explore new approaches, fine-tune them through testing and optimization, and ensure they perform reliably in production. Along the way, you’ll work closely with teammates from diverse technical backgrounds who share a passion for solving complex problems with AI.

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What You’ll Work On

• Develop, fine-tune, and evaluate computer vision deep learning models for different tasks and applications.
• Carry out applied research in deep learning and transfer those findings into production-ready solutions.
• Process and prepare image datasets through pre-processing, cleaning, and quality checks, applying a data-centric approach to ensure reliable model inputs.
• Evaluate models using standard metrics, analyze the results, and iterate to improve accuracy and confidence levels.
• Apply optimization methods such as pruning, quantization, and knowledge distillation to make models efficient on edge devices.
• Develop APIs and microservices so that trained models can be served and integrated into real-world systems.
• Work closely with team members to review research papers, share learnings, and continuously improve practices in model development.

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What Sets This Apart

This is not a research-only role.

Your models will run in production - on constrained hardware, under real-world conditions, within regulated financial environments.

You’ll gain end-to-end exposure from data preparation to deployment, growing in independence and ownership as your capability matures.

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Who Thrives Here

Engineers who:

• Have built and applied computer vision models beyond experimentation
• Care about statistical rigor and measurable performance
• Understand the realities of deploying AI on edge systems
• Value clear communication and collaborative problem-solving