The Role
Want to make an application Make sure your CV is up to date, then read the following job specs carefully before applying. As a Senior AI Engineer, you will be in charge of data ingestion and model development to scalable and continuous optimisation in production. you will be responsible for fintech challenges such as fraud detection, risk modelling, transaction monitoring, and intelligent automation, applying advanced engineering practices to build secure, reliable, and high-performance AI systems. Responsibilities Design, develop, and deploy scalable AI/ML solutions for production use cases (e.g. intelligent automation, NLP, computer vision, predictive systems) Architect and maintain end-to-end ML systems and pipelines for training, evaluation, and inference at scale Work with complex, high-volume structured and unstructured datasets across multiple domains Lead the development of robust data preprocessing, feature engineering, and transformation workflows Ensure data quality, integrity, and compliance with data governance frameworks (e.g. GDPR) Optimise models and pipelines for performance, scalability, and cost-efficiency in production environments Collaborate cross-functionally with data engineers, software engineers, and product stakeholders to deliver integrated AI solutions Deploy and manage models using cloud platforms (AWS, Azure, or GCP) and containerisation technologies Establish monitoring, validation, and testing frameworks to ensure reliability and performance of AI systems Drive continuous improvement through experimentation, iteration, and rigorous evaluation Champion MLOps best practices, including CI/CD pipelines, model versioning, observability, and reproducibility Mentor junior engineers and contribute to raising engineering standards across the team Your Profile 5+ years of experience in AI Engineering, Machine Learning Engineering, or related roles Strong programming expertise in Python and production-level software development practices Hands-on experience with ML/DL frameworks (e.g. TensorFlow, PyTorch) Deep understanding of machine learning algorithms, evaluation methodologies, and optimisation techniques Proven track record of designing, deploying, and scaling ML systems in production environments Strong knowledge of data engineering concepts (ETL/ELT, distributed data pipelines) Experience with cloud platforms (AWS, Azure, or GCP) in production settings Experience with xjsrcvq containerisation and orchestration tools (Docker, Kubernetes) Solid understanding of MLOps practices and tools (e.g. MLflow, Airflow, CI/CD pipelines) Experience working with large-scale, complex datasets and distributed systems Strong awareness of data privacy, security, and governance best practices Ability to lead technical initiatives and influence architecture decisions The Offer Competitive salary and comprehensive benefits package Hybrid working environment Opportunity to work on high-impact, scalable AI systems in a modern, engineering-driven environment Clear progression path into technical leadership or principal roles Apply If this opportunity excites you, apply today or send your CV and a short cover letter to