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Job DescriptionPurpose of this Role:The VP and Head of AI Lab leads the bank's AI delivery engine. The role is accountable for executing the bank's AI operating model and strategy, scaling its portfolio of strategic AI assets, and operationalizing AI across the enterprise so that it generates measurable value for customers, employees, and shareholders.The Head of AI Lab oversees two delivery squads that build and run the bank's flagship AI products, while also enabling the wider organization to adopt AI tools, platforms, and capabilities responsibly. The role combines hands-on portfolio leadership with strategic ecosystem building, executive engagement, financial stewardship, and people development.What You Will DoStrategy and Operating Model ExecutionTranslate the bank's AI strategy into an executable portfolio of products, releases, and platform capabilities.Operate the AI operating model end-to-end, from intake and prioritization through delivery, deployment, and continuous improvement.Maintain alignment between the AI Lab roadmap, enterprise priorities, and divisional business plans.Squad and Portfolio LeadershipLead two diverse squads covering customer experience AI products and internal productivity AI solutions, each with distinct missions, talent profiles, and stakeholders.Partner with the AI Delivery Lead to remove blockers, mitigate delivery risk, and ensure on-time release of strategic assets and committed milestones.Empower squad leads and product managers to operate with autonomy, clarity of priorities, and full ownership of outcomes.Democratization and Operationalization of AIEquip the organization with the right AI tools, platforms, and reusable assets to accelerate safe, productive adoption across functions.Build a community of practice and an enablement framework so that teams across the bank can apply AI confidently in their day-to-day work.Drive change management, capability uplift, and cultural reinforcement so AI becomes embedded in everyday ways of working.Stakeholder Management and Executive Reporting.Prepare and run weekly Chief Customer Officer reviews and monthly CEO updates, ensuring material is high quality, aligned with strategic priorities, and prepared well in advance.Build trusted relationships with department heads and squad sponsors to socialize AI opportunities and convert them into prioritized, well-scoped initiatives.Maintain alignment with peer leaders across Enterprise Architecture, Technology Delivery, Risk, Compliance, and other AI-related functions. Partnerships, Ecosystem, and R&DIdentify, evaluate, and onboard external AI providers, technology partners, and strategic investment opportunities to accelerate the bank's AI ambitions.Build and nurture relationships across the regional and global AI ecosystem to bring leading capabilities into the bank.Sustain a meaningful R&D capability within the Lab, whether through in-house experimentation, academic collaborations, or co-development with vendors and partners.Agile Practice and Cross-Functional Alignment.Operate as an active member of the Agile community, working closely with the Enterprise Project Management Office (EPMO), the Agile practice, and the relevant chapters.Synchronize AI Lab ceremonies, releases, and planning cycles with enterprise delivery rhythms and quarterly business reviews. People Leadership and DevelopmentBuild, coach, and retain a high-performing, diverse team of AI product, delivery, engineering, and science talent.Set clear performance expectations, invest in career development, and shape a culture of accountability, curiosity, and disciplined execution.Financial Accountability.Own the AI Lab's CapEx and OpEx envelope in collaboration with the AI Delivery Lead and Finance partners.Ensure the program is delivered within budget and that investment decisions are tied to measurable business outcomes. Decision-Making AuthorityPrioritization and sequencing of the AI Lab portfolio within the agreed strategic envelope.Selection of AI tooling, platforms, and architectural patterns within enterprise governance.Selection and onboarding of AI vendors, partners, and research collaborators.Resourcing decisions across the squads, including hiring, talent allocation, and role design.Release and go-live decisions for AI products, in coordination with Risk, Compliance, and Technology partners.What We Are Looking ForBachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative or technology discipline. A Master's or post-graduate qualification in AI, Data Science, or a related field is a plus but not required.10 to 12 years of progressive experience across AI, data science, advanced analytics, or technology product delivery, with at least 3 to 4 years leading teams of engineers, data scientists, or AI product talent.Hands-on experience delivering AI or analytics products end-to-end, from concept through production and adoption. Direct experience with Generative AI is preferred.Experience working in banking, financial services, or another regulated industry is preferred. Exceptional candidates from technology, consulting, or product backgrounds with strong domain partners will also be considered.Demonstrated ability to manage a portfolio of initiatives, executive-level stakeholders, and a meaningful budget.Comfortable operating inside an agile delivery environment and partnering with product, engineering, and control functions.Required CompetenciesAI and Machine Learning Strategy: ExpertGenerative AI, Large Language Models, and Agentic Systems: ExpertAI Product Delivery and MLOps: ExpertCloud and Data Platforms: AdvancedAI Governance, Responsible AI, and Model Risk: AdvancedSolution Architecture for AI: AdvancedCybersecurity and Data Privacy Awareness: Advanced

Job DescriptionBuild Smart Systems. Automate Learning. Turn Data into Insight.We’re seeking a highly technical and innovative Learning Technology & AI Solutions Specialist to support and evolve a modern digital learning ecosystem. This role sits at the intersection of learning platforms, AI, automation, and data analytics, helping to improve how training is delivered, measured, and experienced across the network.You’ll work across LMS systems, microlearning platforms, automation workflows, and AI tools—building solutions that improve engagement, reduce manual effort, and deliver meaningful insights to the business.What You’ll Do Manage and optimize learning platforms (LMS and Microlearning), including user accounts, course setup, campaigns, and troubleshooting Design and build AI-enabled solutions such as learning assistants, Q&A bots, reporting summaries, and training insights tools Develop automation workflows and scripts (Python, Power Automate, APIs) to streamline manual processes like provisioning, reporting, and campaign scheduling Create engaging microlearning content, including quizzes, challenges, and scenario-based learning aligned to business goals Build and maintain RAG workflows, LLM integrations, and AI agents (LangChain, LangGraph) connected to training knowledge sources Maintain and enhance reporting tools, including Power BI dashboards and automated reporting applications (Streamlit / HTML) Extract, clean, and analyze training data from LMS, microlearning platforms, and databases to identify trends and performance gaps Automate and streamline recurring data processes such as reporting, reconciliation, and survey analysis Support training measurement, including survey analysis, KPI tracking, and performance insights Lead stakeholder communications, including dealer follow-ups, reporting support, and training guidance Present insights, dashboards, and AI initiatives to leadership in a clear, business-focused way Identify new opportunities to improve engagement, automation, and platform effectiveness What Makes You a Great Fit Experience working across learning platforms, data, or digital training environments Strong skills in Python, Power BI, and Excel for data processing, reporting, and automation Hands-on experience with Generative AI, LLMs, or AI tools applied to real business use cases Familiarity with RAG, prompt engineering, or AI workflows (LangChain, vector databases, etc.) Experience building automation solutions (scripts, APIs, Power Automate, or similar tools) Strong analytical mindset—able to turn raw data into clear business insights Comfortable working with both technical systems and non-technical stakeholders Ability to manage multiple priorities and work independently in a fast-paced environment Strong communication skills, including the ability to present technical concepts clearly Background in training, learning operations, or digital content development is a plus Why This Role Stands OutThis is not a traditional L&D or data role—you’ll be building the systems that modernize how learning operates. From AI agents to automated reporting and personalized content, your work will directly shape how training scales, engages users, and drives performance.

Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.Role OverviewWe are seeking an experienced AI Data Engineer to design, develop, and deploy innovative data and machine learning solutions that drive customer engagement, retention, and lifetime value (LTV). This role will focus on building scalable data pipelines, predictive models, and GenAI-powered applications that enable more personalised trading experiences and support data-driven decision-making across the business.Key ResponsibilitiesCollaborate with the wider marketing department to build meaningful datasets and a centralised data warehouse that will be used for reporting across numerous teams, focused on customer retention and LTV. Develop and own machine learning solutions that specialise in customer personalisation to increase engagement and coordinate with marketing teams towards their strategy and execution across multiple channels, especially around predictive modelling, churn prediction and customer segmentation. Explore the capabilities of Gen AI to develop new apps for our traders that make trading easier and more personalised based on their preferences. Maintain a commercial acumen and find opportunities where technology can be used to increase customer engagement or automate internal tasks. Qualifications & RequirementsStrong expertise in SQL, comfortable with raw data and multiple high-volume datasets such as customer journey, marketing performance or product behaviour. Comfortable creating pipelines and automations with Python to connect multiple endpoints and APIs such as first party databases, marketing platforms or other data collection platforms. Expertise in using Python todeploy machine learning solutions end to end, especially around predictive modelling and customer segmentation. Previous experience in finding customer insights through data analysis, communicating it to different stakeholders and strategizing on how to increase customer engagement and LTV. Capable of creating applications using LLMs that summarise high volumes of data and knowledge of the latest Gen AI use cases. Previous exposure to ETL tools (e.g., dbt, AWS Glue) and cloud environments (preferably AWS, SageMaker). Strong communication skills with the ability to present complex data insights to non-technical stakeholders and support business strategy. Why Join Us?Work with an industry-leading global financial institution. Competitive salary and comprehensive employee benefits. Opportunities for professional growth and career advancement. Collaborative, inclusive, and dynamic work environment. Commitment to innovation and professional excellence. Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.MultiBank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.

The Machine Learning Engineer reports to the Group Service Support Specialist and takes ownership of the key function of Data Science and ML engineering,This role primary function is to turn raw fleet telemetry, scheduling inputs and operational data into decisions, predictions, and insight that improve the performance of mining operations.This is the foundational analytical hire - responsible for exploring data, defining problems, collaborating with domain experts, and building models that inform the product roadmap across predictive maintenance, anomaly detection, operator behaviour, and dispatch optimisation.Typical features to commercialise are multivariate anomaly detection, failure prediction, estimate remaining life prediction, schedule optimisation, OR(Vehicle routing problem), Computer vision solutions.The role is expected to be able to take ownership of the entire chain of commercialised ML features in the product, including formulating problems, identifying and implementing data infrastructure requirements and implementing ML operational models.Job Overview:Data exploration and problem formulationConduct structured data audits on incoming data streams, assessing signal quality, completeness, historical depth, sampling frequency, and suitability for each modelling use caseTranslate ambiguous business questions into scoped problem statements defining target variable, features, data requirements, modelling approach, and success criteriaDistinguish between problems requiring machine learning, statistical analysis, or rules-based logic and recommend the right approach for each.Maintain a prioritised problem inventory with documented assessments of data readiness, tractability, effort, and business value.Identify data inventory deficiencies and formulate plans to solve and or retrieve.Data Architecture and EngineeringDesign and implement ingestion pipelinesBuild and maintain ETL pipelines transforming raw sensor streams into clean, structured, model-ready feature setsDesign data warehousing and architecture that is fit for purpose.Monitor pipeline health, data freshness, and schema drift as the fleet and product evolve.Vender selection and justification with respect to the expected outputs and associated costsModel Development and ValidationMaintain a structured model registry tracking all experiments, versions, hyperparameters, and evaluation metrics to ensure full reproducibility of any model at any point in time.Implement monitoring frameworks for deployed models that track prediction quality, input data distributions, and output stability over time.Define and document retraining protocols for each deployed model specifying trigger conditions, data requirements, and the validation criteria a model must pass before replacing the current version.Collaborate with engineering to package validated models into production-ready artefacts with clearly documented input formats, output schemas, and failure handling expectationsEvaluate and recommend appropriate deployment patterns for each model based on latency, reliability, and operational requirements.ML Ops and production readinessValidated and commercially viable models to be developed into production ready solutions for the product.Track all experiments, model versions, hyperparameters, and evaluation metrics in a structured model registry ensuring full reproducibilityImplement monitoring frameworks for deployed models tracking prediction quality, input distributions, and output drift over timeEstablish automated alerting for model degradation and define retraining triggers based on performance thresholdsDesign retraining protocols specifying trigger conditions, training data windows, and validation gates a model must pass before replacing the current versionCollaborate with engineering to package validated models into production-ready artefacts with clear input formats, output schemas, and failure handling specificationsEvaluate and recommend appropriate serving infrastructure for each model - batch, near-real-time API, or edge deployment - based on operational requirements Communication Insights and stakeholder engagementPresent model findings and analytical results to non-technical leadership in plain language with honest confidence estimates and clear business implications.Communicate proactively when data assumptions underpinning a modelling initiative are not met, recommending corrective action rather than proceeding on a weak foundation.Document all analytical decisions, model assumptions, and data limitations in a form accessible to engineering, product, and future data science hires. Skills, Knowledge & Attributes Required:Bachelor's degree in engineering, mathematics, computer science, data science, or statistics.Postgraduate qualification in data science, machine learning, or applied mathematics is strongly preferred.Minimum 5 years in an applied data science or analytical role.Strong foundations in statistics, probability, and applied mathematics.Proven experience with industrial IoT or operational sensor data - time series analysis, signal quality assessment, anomaly detection, and predictive modelling.Proficient in Python and SQL; experienced with Kafka or MQTT, cloud platforms, Snowflake, Databricks, and Git.Solid understanding of MLOps practices including experiment tracking, model versioning, monitoring, and deployment.Experience with linear or mixed-integer programming is advantageous.Exposure to LLM, NLP, or agentic AI is advantageous but not required.

Job Purpose: A skilled and experienced Machine Learning Engineer to join our innovative team. The ideal candidate will have strong expertise in research and development (R&D), as well as the practical implementation of robust AI models.The role requires a deep understanding of machine learning (ML), deep learning (DL), Generative AI models, including Large Language Models (LLMs), natural language processing (NLP), and speech analysis and recognition. The candidate should also have strong Python and Java programming skills, scripting experience, proficiency in ML frameworks and tools, microservices, Docker containers, automated ML pipelines, and basic DevOps practices.Job Responsibilities: R&D Experimentation: Conduct cutting-edge research on ML, DL, Generative AI models, NLP, and speech recognition technologies to drive innovation and improve our AI solutions.Model Development: crawl data from various datasources, develop pipelines, build models, train, and optimize robust AI models using state-of-the-art techniques in NLP, speech analysis, and recognition.AI Service Packaging: Package AI models as services, ensuring they are ready for deployment in production environments.Deployment: Deploy AI services using microservices architecture and Docker containers for scalable and reliable operation.Automated Pipelines: Design and implement automated machine learning pipelines for model training, testing, and inference.Scalable Deployments: Develop and manage scalable deployments and distributed training processes to handle large-scale data and models.Performance Monitoring: Continuously monitor and evaluate model performance, making necessary adjustments to improve accuracy and efficiency.Collaboration: Work closely with cross-functional teams, including data scientists, product managers, and software engineers, to deliver high-quality AI solutions.Technical Skills:Deep understanding of ML, DL, Generative AI models, NLP, and speech analysis and recognition.Proficiency in Python and Java programming and strong coding skills.Experience with microservices architecture and Docker containers.Expertise in automated ML pipelines for training, testing, and inference.Knowledge of scalable deployments and distributed training techniques.Familiarity with Ubuntu server commands and basic DevOps skills.Preferred Educational Qualifications and Professional CertificationsBachelor’s degree in Artificial Intelligence, Computer Science, or a relevant field.ExperienceUAE National fresh graduates are encouraged to apply. 1–2 years of experience in AI/ML is required. Proven experience in machine learning engineering, with a focus on R&D and production-level deployment. Extensive hands-on experience with ML frameworks and tools such as TensorFlow, PyTorch, etc.

We are looking for a Data Scientist to join the Business Analytics team at Spotify. You will work with the Regional Analytics Lead to evaluate and influence our growth strategy in Middle East & Africa exciting and diverse landscape.You will collaborate with a distributed team of world-class analysts, data scientists, business leaders, marketers, and engineers. Learning and improving is part of our daily routine, and you will get a platform to develop your data skills and carve out efficient ways of working.The Business Analytics team is part of Spotify’s core business strategy organization. You’ll play a crucial role in the growth and direction of Spotify as we grow to 760M+ users around the globe.At your fingertips, you’ll have access to all the data Spotify has to offer, and the opportunity to be creative with how you use it to derive insights and strategies. Above all, your work will impact the way the world experiences audio (and video)!What You'll DoDevelop data driven strategies to drive the growth of Spotify users and subscribersIdentify new user growth levers in the SAMEA region and use experimentation to help drive that growthCreate and communicate impactful recommendations and models that improve our product offering, user messaging, and channel optimizationBuild scalable data pipelines and dashboards to facilitate business performance trackingWork closely with business partners to understand the change they are driving and help them discover new opportunities for growthDesign and implement comprehensive tests, making sure that we track all relevant metrics and that we’re learning at every step along the wayPresent your findings to senior collaborators, influencing the course of our businessWho You AreYou have a least 2+ years of professional experience synthesizing insights from data using tools such as Python/R, SQLExperience with distributed systems and cloud data platforms (BigQuery or GCP) is a plusExperience with AI tools (LLMs and coding assistants) is a plusIntellectually curious, creative, and diligent - you enjoy thinking about the business as much as about the dataHave experience collaborating with partners to measure the impact of business/marketing initiatives and presenting those findings in coherent recommendationsHave a background in Economics, Computer Science, Statistics, Engineering or a relevant fieldComfortable working on a globally distributed team (with occasional international travel)Relevant experience in a consumer tech/product company is a plusWhere You'll BeThis role will be based at Spotify’s office in DubaiWe offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from homeWe may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Find our AI notice here: https://lifeatspotify.com/ai-noticeToday, we are the world’s most popular audio streaming subscription service.