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.
What You'll Do
- 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.
What we’re looking for
2–3 years practical experience in computer vision and deep learning, with a proven track record of building and applying models in real projects.Strong programming skills in Python , with hands-on use of frameworks such as PyTorch or TensorFlow .Solid understanding of statistics, including the ability to design experiments, run tests, analyze results, and interpret findings to guide model improvements.Familiarity with edge computing hardware such as Jetson, TPU, or FPGA, and the ability to optimize models to run efficiently on these devices.Knowledge of model optimization methods such as pruning, quantization, or distillation, and how to apply them to reduce model size, improve speed, and maintain accuracy for deployment on edge systems .Ability to evaluate and report model performance using appropriate metrics (e.g., precision, recall, F1 score, latency, throughput), while embracing a data-centric approach to AI modelling to continually improve outcomes .Clear communication skills and the willingness to collaborate with a multi-disciplinary team.A degree in Computer Science, Data Science, Artificial Intelligence, or a related field (or equivalent practical experience).Hands-on experience applying computer vision and deep learning in real projects, including model training and evaluation.Exposure to deploying models on edge devices (e.g., Jetson, TPU, FPGA) and to production-level software practices such as version control, testing, or CI / CD.Why Join Us?
Work on AI models that move beyond theory — your code will run in real-world systems where accuracy, speed, and reliability matter.Join a tight-knit R&D team led by seasoned AI and computer science experts, where you’ll have space to learn, experiment, and grow through direct hands-on mentorship.Advance from IC2 to IC3 , taking on more independence and project ownership as your skills mature.Build your craft end-to-end from data to deployment, from research paper to production-ready model – See your work deployed in production systems where every improvement in accuracy, speed, or reliability makes a real difference.#J-18808-Ljbffr