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