Ai engineer | qatar

Contract duration:1 year (extension possible)Start Date:ASAPExperience: 4–6 yearsVisa Sponsorship:Available if neededKey AccountabilitiesDesign and develop machine learning models for pricing optimization, including dynamic pricing, rate optimization, and fee structuresBuild propensity models for customer behavior prediction, including churn, cross-sell, upsell, and product adoptionDevelop recommendation systems for personalized product offerings, next-best-action, and customer engagementBanking Domain ApplicationApply deep banking domain knowledge to frame business problems as machine learning solutions with measurable outcomesPartner with Risk, Finance, and business units to identify high-value modelling opportunitiesEnsure models incorporate relevant regulatory requirements, risk considerations, and business constraintsAnalysis & InsightsConduct exploratory data analysis to identify patterns, relationships, and modelling opportunities in banking data.Translate model outputs into actionable business recommendations and insightsDevelop model performance metrics aligned with business KPIs and financial outcomesCreate data visualizations and reports for stakeholder communicationPrototyping & DeliveryDevelop working prototypes in Python demonstrating model functionality and business valueCreate clear documentation of model methodology, assumptions, limitations, and use casesCollaborate with ML Engineers and AI Engineers to transition prototypes into production systemsStakeholder Collaboration & GovernancePartner with business stakeholders to understand requirements and validate model outputsPresent model results, methodology, and recommendations to senior managementContribute to model governance, validation, and documentation requirementsEnsure compliance with data policies, ethical standards, and regulatory requirementsExpert knowledge of supervised and unsupervised learning techniques for classification, regression, and clusteringDeep experience with pricing models, propensity modelling, and recommendation systemsStrong foundation in statistical analysis, hypothesis testing, and experimental designFamiliarity with deep learning frameworks such as Tensor Flow and Py TorchBanking Domain ExpertiseComprehensive understanding of banking products (Retail or Corporate), services, and customer lifecycleKnowledge of risk functions, including credit risk, market risk, and operational risk frameworksUnderstanding of Finance functions, including P&L drivers, cost allocation, and profitability analysisFamiliarity with regulatory requirements impacting model development (e.g., IFRS 9, Basel)Ability to translate complex analytical concepts into business language for non-technical stakeholdersExperience working with cross-functional business and technology teamsExperience with Agile methodologies (Kanban, Scrum)RequirementsBachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field.4–6 years of experience in software engineering, with at least 2 years focused on AI/ML applications.Hands-on experience with cloud platforms (Azure or GCP) and containerization (Docker, Open Shift/K8s).Experience with document processing, metadata extraction, and knowledge management systems preferred.Banking or financial services industry experience is a plus.Relevant certifications (Azure AI Engineer, GCP ML Engineer) preferred.