The RoleAs 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.ResponsibilitiesDesign, 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 scaleWork with complex, high-volume structured and unstructured datasets across multiple domainsLead the development of robust data preprocessing, feature engineering, and transformation workflowsEnsure data quality, integrity, and compliance with data governance frameworks (e.G. GDPR)Optimise models and pipelines for performance, scalability, and cost-efficiency in production environmentsCollaborate cross-functionally with data engineers, software engineers, and product stakeholders to deliver integrated AI solutionsDeploy and manage models using cloud platforms (AWS, Azure, or GCP) and containerisation technologiesEstablish monitoring, validation, and testing frameworks to ensure reliability and performance of AI systemsDrive continuous improvement through experimentation, iteration, and rigorous evaluationChampion MLOps best practices, including CI/CD pipelines, model versioning, observability, and reproducibilityMentor junior engineers and contribute to raising engineering standards across the teamYour Profile5+ years of experience in AI Engineering, Machine Learning Engineering, or related rolesStrong programming expertise in Python and production-level software development practicesHands-on experience with ML/DL frameworks (e.G. TensorFlow, PyTorch)Deep understanding of machine learning algorithms, evaluation methodologies, and optimisation techniquesProven track record of designing, deploying, and scaling ML systems in production environmentsStrong knowledge of data engineering concepts (ETL/ELT, distributed data pipelines)Experience with cloud platforms (AWS, Azure, or GCP) in production settingsExperience with 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 systemsStrong awareness of data privacy, security, and governance best practicesAbility to lead technical initiatives and influence architecture decisionsThe OfferCompetitive salary and comprehensive benefits packageHybrid working environmentOpportunity to work on high-impact, scalable AI systems in a modern, engineering-driven environmentClear progression path into technical leadership or principal rolesApplyIf this opportunity excites you, apply today or send your CV and a short cover letter to ryan.martin@vividresourcing.com