AI / ML Engineer - 0–12+ Years Experience

Please read the JD carefully berore applying.Job Description – AI / ML Engineer  | 0–12+ Years ExperiencePositionAI / ML Engineer (T1–T5)LocationRiyadh, Kingdom of Saudi Arabia (KSA)Relocation Required: YesExperience0–12+ YearsJob SummaryWe are seeking AI / ML Engineers across multiple experience levels (T1–T5) to design, develop, train, deploy, and optimize machine learning models and AI solutions throughout the complete machine learning lifecycle. Candidates will work on data preparation, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement using modern cloud AI platforms and open-source machine learning frameworks.The role offers opportunities ranging from entry-level implementation to enterprise AI architecture and technical leadership.Key ResponsibilitiesDesign, develop, train, evaluate, and deploy machine learning and AI solutionsBuild scalable ML pipelines from data preparation through production deploymentDevelop supervised, unsupervised, deep learning, and generative AI modelsPerform feature engineering, data preprocessing, model validation, and hyperparameter optimizationIntegrate ML models into enterprise applications and cloud-native environmentsDeploy AI models using managed cloud ML services and MLOps practicesMonitor model performance, drift, accuracy, and production reliabilityCollaborate with Data Scientists, Data Engineers, Software Engineers, and DevOps teamsOptimize model performance, scalability, and inference latencyDocument models, experiments, evaluation metrics, deployment processes, and governance standardsFollow AI security, responsible AI, and model governance best practicesRequired Technical SkillsCloud AI PlatformsGCP Vertex AI or BigQuery ML or DataflowAzure ML or Azure OpenAIAWS SageMaker or Amazon BedrockProgrammingPythonMachine Learning FrameworksTensorFlow or PyTorchGenerative AI & LLM FrameworksHuggingFace or LangChainData & AnalyticsDatabricksAdditional SkillsMachine LearningDeep LearningNLPComputer VisionModel EvaluationFeature EngineeringAPI DevelopmentGitResponsibilities by TierT1 – Associate AI / ML Engineer (0–2 Years)Role Focus: Learning, implementation, and execution under supervision.ResponsibilitiesAssist in data preparation, cleansing, and feature engineeringDevelop simple machine learning models using established frameworksSupport model training, testing, and validation activitiesDeploy models under senior guidanceMaintain documentation for datasets, experiments, and modelsDebug ML pipelines and resolve basic issuesLearn cloud AI platforms and development best practicesFollow coding standards, security policies, and project guidelinesT2 – AI / ML Engineer (2–4 Years)Role Focus: Independent development and delivery.ResponsibilitiesBuild and deploy production-ready machine learning modelsPerform feature engineering and model optimizationDevelop reusable ML components and inference APIsImplement model evaluation and performance monitoringIntegrate ML models into enterprise applicationsCollaborate with cross-functional engineering teamsTroubleshoot production AI issuesContribute to model documentation and deployment automationT3 – Senior AI / ML Engineer (5–7 Years)Role Focus: Technical ownership and solution development.ResponsibilitiesDesign end-to-end AI and machine learning solutionsLead development of complex ML pipelines and AI applicationsOptimize training pipelines for performance and scalabilityGuide junior engineers and perform technical reviewsImplement Responsible AI, explainability, and governance practicesImprove model reliability, monitoring, and lifecycle managementCollaborate with business stakeholders to translate requirements into AI solutionsSupport architecture decisions for enterprise AI initiativesT4 – Lead AI / ML Engineer (8–11 Years)Role Focus: Technical leadership and enterprise solution delivery.ResponsibilitiesLead architecture and delivery of enterprise AI platforms and machine learning solutionsDefine technical standards, reusable frameworks, and engineering best practicesLead multiple AI initiatives across business domainsDrive cloud-native AI solution design and deploymentReview solution architecture, model performance, and production readinessMentor engineering teams and provide technical leadershipCollaborate with enterprise architects, product owners, and business leadersImprove AI platform scalability, governance, security, and operational excellenceT5 – Principal AI / ML Architect (12+ Years)Role Focus: Enterprise AI strategy, architecture, and innovation.ResponsibilitiesDefine enterprise AI/ML strategy and long-term technology roadmapOwn architecture decisions for large-scale AI and machine learning platformsLead enterprise-wide AI transformation initiativesEstablish standards for Responsible AI, governance, security, and complianceEvaluate emerging AI technologies, frameworks, and cloud servicesDrive innovation in Generative AI, LLMs, and advanced machine learning solutionsProvide executive-level technical guidance and strategic recommendationsLead technical communities, architecture reviews, and cross-functional AI governanceInfluence organizational AI adoption and engineering excellence across multiple programsPreferred CertificationsOne or more of the following certifications is highly preferred:Google Professional Machine Learning EngineerAWS Certified Machine Learning – SpecialtyMicrosoft Certified: Azure AI Engineer AssociateTensorFlow Developer CertificateExpected DeliverablesMachine Learning Model DocumentationModel Training & Evaluation ReportsFeature Engineering DocumentationModel CardsProduction Deployment PipelinesMonitoring & Performance DashboardsAI Solution Design DocumentsModel Validation ReportsInference APIsProduction-Ready ML ModelsPreferred QualificationsBachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related fieldStrong understanding of statistics, machine learning algorithms, deep learning, and generative AI conceptsExperience with cloud AI platforms and modern ML frameworksKnowledge of MLOps, CI/CD, model deployment, and production monitoring is an advantageStrong analytical, communication, and problem-solving skillsAbility to work in Agile, cross-functional, and enterprise-scale environments