Senior Machine Learning Engineer بدوام كامل في الإمارات العربية المتحدة
التخصص
تكنولوجيا المعلومات
نوع الدوام
دوام كامل
الخبرة
أي مستوى
الموقع
الإمارات العربية المتحدة
وصف الوظيفة
We're Hiring: Senior Machine Learning EngineerLocation: United Arab Emirates (Remote)Employment Type: Full-TimeExperience Level: SeniorWork Arrangement: Fully RemoteAbout UsWe are a globally focused organization committed to developing intelligent technologies, data-driven products, and scalable machine-learning solutions that improve business performance, operational efficiency, and customer experiences across diverse markets.Our multidisciplinary teams collaborate across Artificial Intelligence, Data Science, Software Engineering, Product, Research, Technology, Operations, and Commercial functions to transform complex data into reliable machine-learning systems and measurable business outcomes.The RoleWe are seeking an experienced Senior Machine Learning Engineer to lead the design, development, deployment, optimization, and lifecycle management of production-grade machine-learning systems.The ideal candidate will combine strong expertise in machine learning, deep learning, software engineering, data engineering, and MLOps to develop scalable models and intelligent applications, establish robust model-development practices, and translate advanced analytical capabilities into reliable products and business solutions.Key ResponsibilitiesLead machine-learning projects from problem definition and data preparation through model development, deployment, monitoring, and continuous improvement.Translate business, product, operational, and technical requirements into machine-learning solutions.Identify suitable machine-learning approaches based on business objectives, data availability, technical constraints, and expected outcomes.Design and implement end-to-end machine-learning pipelines for training, validation, inference, deployment, and monitoring.Develop supervised, unsupervised, semi-supervised, and reinforcement-learning solutions where appropriate.Develop predictive models for classification, regression, forecasting, recommendation, ranking, anomaly detection, and optimization.Design and develop deep-learning solutions using appropriate architectures and frameworks.Work with structured, unstructured, time-series, text, image, audio, and other complex datasets.Conduct exploratory data analysis and identify patterns, relationships, anomalies, and data-quality issues.Develop data preprocessing, feature-engineering, transformation, sampling, and enrichment pipelines.Design and maintain high-quality training, validation, and test datasets.Analyze dataset coverage, class imbalance, missing values, outliers, bias, and distribution shifts.Develop strategies for data augmentation, synthetic data generation, sampling, and hard-example mining.Train, fine-tune, evaluate, and optimize machine-learning models.Conduct hyperparameter optimization and systematic experimentation.Establish appropriate model evaluation metrics and validation methodologies.Evaluate model performance using accuracy, precision, recall, F1 score, ROC-AUC, calibration, ranking metrics, forecasting metrics, and other relevant measures.Conduct model error analysis and investigate false positives, false negatives, drift, instability, and other performance issues.Perform cross-validation, statistical testing, sensitivity analysis, robustness testing, and model validation.Compare alternative algorithms, architectures, features, datasets, and modeling approaches.Optimize models for accuracy, generalization, latency, memory usage, computational efficiency, and scalability.Implement model optimization techniques such as quantization, pruning, distillation, feature selection, and efficient inference where appropriate.Develop production-ready machine-learning services, APIs, inference pipelines, and supporting software components.Deploy machine-learning models across cloud, on-premises, edge, or distributed computing environments.Collaborate with Software Engineering and Data Engineering teams to integrate models into production applications.Establish CI/CD and automated testing practices for machine-learning workflows.Implement experiment tracking, model versioning, dataset versioning, and reproducible development processes.Establish model monitoring for performance, data drift, concept drift, latency, availability, and operational health.Develop automated processes for model retraining, validation, deployment, and rollback where appropriate.Establish machine-learning model lifecycle-management standards and governance processes.Maintain technical documentation covering datasets, features, models, experiments, assumptions, dependencies, and deployment configurations.Develop dashboards and monitoring tools to track model and system performance.Investigate production model failures and coordinate root-cause analysis and remediation.Work with Product teams to define model requirements, acceptance criteria, performance targets, and release plans.Collaborate with Data Scientists and Researchers to transition experimental models into production-grade systems.Work with Data Engineers to establish reliable data pipelines and scalable feature-generation processes.Collaborate with AI and Research teams to evaluate emerging machine-learning techniques and technologies.Support applications involving recommendation systems, forecasting, personalization, fraud detection, risk modeling, optimization, natural language processing, computer vision, or intelligent automation where applicable.Evaluate foundation models, generative AI, embeddings, multimodal models, and other emerging machine-learning technologies.Assess open-source and commercial machine-learning frameworks, platforms, models, and infrastructure.Conduct proof-of-concept development and technical feasibility assessments for new machine-learning capabilities.Establish benchmarks for model performance, infrastructure efficiency, and production reliability.Ensure machine-learning systems meet applicable privacy, security, compliance, and responsible-AI requirements.Assess model bias, fairness, explainability, and potential unintended consequences where relevant.Implement appropriate safeguards for sensitive data and machine-learning applications.Manage external technology vendors, consultants, data providers, and specialist partners where required.Mentor machine-learning engineers, data scientists, and junior technical professionals.Conduct code, model, architecture, and technical design reviews.Establish engineering standards, reusable components, development guidelines, and best practices.Monitor developments in machine learning, artificial intelligence, MLOps, cloud computing, and model-development technologies.Provide technical leadership on complex machine-learning architecture and engineering challenges.Provide management with regular updates on model performance, project delivery, technical risks, infrastructure requirements, and improvement opportunities.Key Performance IndicatorsModel accuracyPrecision and recallF1 scoreROC-AUCForecasting accuracyModel calibrationModel generalizationModel robustnessPrediction latencyInference throughputModel availabilityProduction model reliabilityData-quality performanceFeature-pipeline reliabilityData-drift detectionConcept-drift detectionModel degradation rateModel retraining successMachine-learning project deliveryModel development cycle timeExperimentation cycle timePrototype-to-production conversionModel deployment success rateAutomated testing coverageModel monitoring coverageExperiment reproducibilityModel versioning complianceInfrastructure utilizationCompute-cost efficiencyCloud resource efficiencyModel optimization improvementsProduction incident rateMean time to resolve model incidentsTechnical debt reductionMachine-learning pipeline automationStakeholder satisfactionBusiness impact from machine-learning solutionsProcess automation contributionRevenue or cost impact where applicableResponsible-AI complianceSecurity and privacy complianceTechnical documentation completenessTechnology evaluation comple
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