AI Engineer

As an AI Engineer you will be working for our client, a global leader in the banking industry, supporting digital transformation initiatives across multiple business units. You will design and implement AI-driven solutions that optimize decision-making, enhance customer experience, and improve operational efficiency. The role involves working on projects such as intelligent document processing, fraud detection, and predictive analytics, while collaborating with cross-functional teams. This position provides a unique opportunity to apply cutting-edge machine learning models and generative AI techniques in a highly regulated financial environment.

Your main responsibilities:

• Design, develop, and deploy AI and machine learning models for banking use cases

• Collaborate with business analysts and stakeholders to translate requirements into AI solutions

• Integrate AI models into production systems, ensuring scalability and reliability

• Monitor, evaluate, and improve the performance of deployed models

• Work with structured and unstructured financial data to build predictive insights

• Apply generative AI and natural language processing techniques to improve customer-facing services

• Ensure compliance with data privacy, security, and regulatory standards in all AI solutions

• Document processes, models, and frameworks to support transparency and knowledge sharing

• Collaborate with DevOps teams to streamline MLOps and deployment pipelines

• Stay up to date with emerging AI technologies and propose innovative applications

You’re ideal for this role if you have:

• Minimum 4 years of experience as a Machine Learning Engineer or AI Engineer

• Strong proficiency in Python and libraries such as TensorFlow, PyTorch, or Scikit-learn

• Proven experience in building, training, and deploying machine learning models in production

• Familiarity with NLP techniques and large language models (LLMs)

• Experience working with cloud-based AI environments (AWS, Azure, or GCP)

• Solid understanding of MLOps practices and tools such as MLflow or Kubeflow

• Good knowledge of SQL and experience handling large datasets

• Strong problem-solving skills and the ability to explain complex models to non-technical stakeholders

• Fluency in English, both written and spoken

• Excellent communication and collaboration skills across multidisciplinary teams