AI ENGINEER

Company OverviewWe are partnering with a leading banking client to build next-generation AI and machine learning platforms. You will join a high-impact agile team within the ML & AI domain, working on production-grade systems that power intelligent, scalable digital solutions.Job DescriptionWe are seeking skilled Machine Learning Platform Engineers (MLOps / AI Engineers) to bridge the gap between experimental data science and production-ready systems.In this role, you will contribute across the full ML lifecycle — from concept to deployment — and work closely with data scientists, engineers, and infrastructure teams to deliver robust AI-driven solutions. You will also support the development of agentic AI workflows and LLM-powered systems, enabling scalable and autonomous capabilities.Key ResponsibilitiesDesign, develop, and deploy scalable machine learning solutions and servicesBuild and maintain end-to-end ML pipelines (data ingestion, training, validation, deployment, monitoring)Operationalise LLMs, embeddings, and multi-agent systems in production environmentsDesign and implement Retrieval-Augmented Generation (RAG) systemsManage full model lifecycle (experimentation, model registry, deployment, monitoring)Oversee model promotion processes, including validation gates and approval workflowsContainerise applications using Docker and orchestrate using Kubernetes (K8s)Build and maintain CI/CD pipelines for ML and AI applicationsCollaborate with data scientists to productionise research code into scalable Python applicationsMonitor model performance, data drift, and system reliability in productionIntegrate AI solutions into existing enterprise systems and infrastructureParticipate in code reviews, testing, and debugging to ensure high-quality deliverablesRequirementsEducationBachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or related fieldTechnical SkillsStrong proficiency in Python (clean, testable, efficient code)Experience with Docker and working knowledge of KubernetesHands-on experience with MLOps tools (e.g., MLflow, Weights & Biases)Experience working with Large Language Models (LLMs)Understanding of AI agents, agentic workflows, and LLM orchestration frameworksExperience with data preprocessing, feature engineering, and model evaluationFamiliarity with CI/CD tools (e.g., GitLab, Jenkins)Understanding of GPU computing and cloud-based ML infrastructureStrong foundation in statistics, probability, and linear algebraExperienceRelevant experience in machine learning, data science, or MLOps rolesExperience deploying models into production environments is highly preferredCompetenciesStrong analytical and problem-solving skillsAbility to work in a fast-paced, agile environmentExcellent communication and stakeholder management skillsStrong attention to detail and code qualitySelf-driven with a continuous learning mindset