Your AI career in the UAE starts here

Summary The role designs, develops and deploys production AI solutions for UXE, concentrating on computer vision, video analytics and edge AI, while also supporting Arabic/English agentic AI initiatives. Job Description Hands-on role covering solution architecture, model development, optimization and integration across UXE's AI projects. Work with software, DevOps, infrastructure and business teams from PoC through deployment.Responsibilities •   Design and evaluate detection, tracking, segmentation, classification and video-analytics solutions.•   Build low-latency multi-camera pipelines for NVIDIA Jetson and GPU platforms.•   Optimize models using ONNX, TensorRT, CUDA, DeepStream or GStreamer and measure accuracy, precision and recall, false alarms, throughput and latency.•   Integrate AI with APIs, databases, VMS, cameras and backend systems; implement monitoring, resilience and rollback so deployed systems are robust, scalable and maintainable.•   Develop and evaluate Arabic/English RAG, LLM and agentic workflows with guardrails and human review.•   Work with cross-functional teams to capture project requirements and deliver AI solutions that meet business needs.•   Lead technical design, code/model reviews, documentation, mentoring, customer workshops and production troubleshooting.•   Track advances in AI, machine learning and computer vision; conduct research and internal PoCs that contribute to the continuous improvement of UXE's AI capabilities.Experience Required •   4 years of professional experience in AI, machine learning or computer vision, including at least 2 years delivering systems into live production.•   Proven track record of taking AI solutions from proof of concept through deployment and ongoing operation.•   Experience with real-time image and video analytics at scale, including tuning against accuracy, latency and throughput targets.•   Exposure to LLM, RAG or agentic AI development, together with technical leadership or mentoring of engineers.•   Experience delivering technology projects in the UAE is strongly preferred.Qualifications •   Bachelor’s or Master’s degree in Computer Science ,Electrical Engineering, AI, Machine Learning, Computer Engineering or a related field.•   Equivalent practical experience may be considered with a strong portfolio of deployed AI systems.•   A Ph.D. in a relevant field is an advantage Technical Skills Required •   Python, PyTorch, OpenCV, Linux, Git, Docker, APIs and production debugging, with disciplined code review and agile delivery practices.•   NVIDIA Jetson, ONNX, TensorRT, and DeepStream; CUDA and C++ are advantageous.•   Cloud platforms (AWS, Google Cloud or Azure) and container orchestration with Kubernetes are advantageous for hybrid edge-cloud deployments.•   LLM/RAG/agent frameworks, vector databases, evaluation and guardrails; Arabic AI experience is preferred.Soft Skills •   Strong ownership, analytical troubleshooting, creative problem-solving and sound technical judgement.•   Clear communication of technical concepts to both technical and non-technical stakeholders, with strong documentation, teamwork and mentoring ability.•   Able to balance model quality, performance, hardware constraints and operational risk.

ABOUT XANTORYXantory runs a vertical-farming control platform that plans, drives and records every crop cycle: climate, light, irrigation and dosing across rooms and racks, from a cloud planning tier down to controllers on the racks. Every reading, every relay switch, every recorded stage boundary and every harvest is stored. What the platform cannot do yet is see the plants.As the first machine-learning engineer on the team, the Machine Learning Engineer – Computer Vision will build that capability: cameras on the racks, models that read the crop, and the pipeline that turns what they see into alerts, records and control decisions. The role will also put the sensor and outcome data already collected to work, and will be measured on what runs in production in front of real crops.Role OverviewYou will own the work end to end. That starts with specifying the imaging setup and building our first labelled datasets on site, then training models for plant counting, growth stages, stress and disease, and harvest readiness, and deploying them at the edge and on the site server. You will also combine vision results with the sensor and yield data we already collect to compare growing recipes on evidence. This is a hands-on, production-focused role based in Dubai with regular on-site farm work. Success is measured by what runs reliably in front of real crops.Key Responsibilities1. Computer Vision (Core of the Role)Specify the imaging setup for racks and trays: camera selection and placement, lighting under grow-light spectra, capture schedule, and the image and annotation standards that make a dataset trustworthy.Build the farm's first labelled image datasets from our own crops, starting with data collection on site.Train and deploy models for plant detection and counting, germination and growth-stage recognition, canopy coverage, stress and disease indication, and harvest readiness.Choose the right approach per problem (classification, detection, segmentation, anomaly detection) and prove it with evaluation that holds up on new crops and new racks.Deploy inference at the edge (Raspberry Pi-class devices today, Jetson-class where justified) and on the site server, balancing accuracy, latency and hardware limits.2. Sensor & Outcome DataCombine what the cameras see with time-series data (temperature, humidity, CO₂, PPFD, pH, EC, flow, actuator history, recorded stage boundaries and yields) to explain outcomes against the recipe.Model cycle length and yield per rack against growing recipes and compare recipes based on evidence.3. Integration & OperationShip models as services that fit the platform, including Rust, PostgreSQL and Redpanda for data, Kubernetes for deployment, and results surfaced in Sentinel.Version datasets, experiments and models; monitor model performance and degradation; retrain on production feedback.Define the data contracts your models consume with backend, edge and console engineers.Qualifications & Experience4 years of hands-on computer vision experience with PyTorch or TensorFlow, including at least one vision system taken from your own data collection and kept running.Experience building image datasets from scratch, including quality control, class balance, and the honest handling of small and shifting datasets.Practical experience with cameras and video pipelines, including exposure, colour under artificial light, and calibration.Exposure to horticulture, controlled-environment agriculture (CEA), plant phenotyping or industrial quality inspection is an advantage.Experience with multispectral or NIR imaging is an advantage.Skills & CompetenciesSolid Python and ML fundamentals, including leakage-safe validation.Comfortable in a production engineering environment: Git, code review, tests, containers, and working against services others own.Edge inference experience (ONNX, TensorRT, NVIDIA Jetson, Raspberry Pi deployments) is an advantage.Familiarity with MLOps tooling (experiment tracking, model registry and monitoring) is an advantage.Knowledge of Rust or Go, MQTT and Kubernetes is an advantage.Clear written and spoken English; able to explain a model's limits to a grower and its interface to a backend engineer.How Success Will Be MeasuredProduction deployment: Number of vision models running in production on live racks, and time from data collection to first deployment.Model accuracy: Detection, counting and growth-stage accuracy validated on new crops and new racks, not only the training set.Edge performance: Inference latency, uptime and resource use on Raspberry Pi / Jetson-class devices and the site server.Dataset quality: Coverage of labelled datasets across crops and growth stages, annotation consistency and class balance.Model health: Detection of performance degradation and time to retrain on production feedback.Operational impact: Alerts and records adopted by growers, and yield / cycle-length insights used in recipe decisions.Work Location: In person

Join one of the world's leading multi-strategy investment platforms as a Quant Researcher focused on Machine Learning and AI. This role sits within a highly successful systematic investment team that is investing heavily in next-generation research capabilities and the application of cutting-edge machine learning techniques to financial markets.The team is seeking exceptional researchers who can bring expertise from frontier AI, deep learning, large-scale modelling, and modern machine learning research into a fast-paced environment where ideas are rapidly tested and deployed. This is an opportunity to work on some of the most challenging prediction problems in the world while having direct impact on investment performance.ResponsibilitiesDevelop and implement machine learning models to identify predictive signals across global marketsResearch and apply state-of-the-art deep learning techniques for alpha generationDesign and test neural network architectures for financial forecasting and pattern recognitionWork with large, complex, and alternative datasets to uncover unique sources of investment insightConduct rigorous research, experimentation, and statistical validation of trading signalsCollaborate closely with Portfolio Managers, Quant Researchers, and Data ScientistsBuild scalable research infrastructure and production-ready modelling pipelinesStay at the forefront of developments in AI, machine learning, and quantitative researchContribute to the development of systematic investment strategies using modern ML techniquesRequirementsPhD or Master's degree in Machine Learning, Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative disciplineStrong background in machine learning research and modellingExperience building and deploying deep learning models in real-world environmentsExpertise in areas such as neural networks, representation learning, transformers, foundation models, reinforcement learning, probabilistic modelling, or large-scale optimisationExcellent programming skills in Python and familiarity with modern ML frameworks such as PyTorch or TensorFlowStrong mathematical and statistical foundationsDemonstrated ability to conduct independent research and solve complex problemsExperience from leading AI labs, deep technology companies, research institutions, or high-performance machine learning environments is highly desirablePreferred BackgroundsWe are particularly interested in candidates from:Leading AI research labsFrontier foundation model teamsDeep learning and applied AI organisationsResearch-focused technology companiesQuantitative research groupsHigh-performance computing and data science environmentsAcademic researchers with strong publication records in machine learning or artificial intelligenceWhat You'll GetOpportunity to apply cutting-edge AI research to real-world investment decisionsAccess to significant computational, data, and research resourcesHighly collaborative environment alongside world-class investors and researchersCompetitive compensation package with substantial upside potentialDirect impact on investment outcomes and business growthRelocation support to Dubai where applicableExposure to one of the most intellectually challenging applications of machine learningThis position is ideal for researchers who are passionate about solving difficult prediction problems, building innovative machine learning systems, and applying frontier AI techniques in a highly competitive and rewarding environment. Desired Skills and ExperienceSeeking elite ML Researchers and PhDs to develop deep learning models for alpha generation within a highly capitalised, technology-driven investment team.

Job title: Machine Learning Engineer 1Location: Dubai, UAEAbout noonWe’re building an ecosystem of digital products and services that power everyday life across the Middle East—fast, scalable, and deeply customer-centric. Our mission is to deliver to every door every day. We want to redefine what technology can do in this region, and we’re looking for a (add title) who can help us move even faster.noon’s mission: Every door, every day.What you'll do:Team noon has some of the fastest, smartest, and hardest-working people we've encountered. As a Machine Learning Engineer (MLE1), Ads, you will be a core individual contributor responsible for the hands-on design, development, and deployment of production-grade Machine Learning models across our Ads division. You will be responsible for delivering high-impact models and deployments that power critical functions like ranking, bidding, and click-fraud detection.This role is deeply technical; you will partner with Product and Engineering teams across noon Ads to turn complex data into competitive advantagesDeeply engage in the technical work: building, training, and deploying production-grade models across our domainsContribute to solving "hard" problems involving relevance, ranking, bidding optimization, and fraud detectionPartner with the broader engineering team to implement optimal deployment strategies for heavy ML workloadsAdhere to and contribute to the standardization of ML Ops and best practices within the teamWhat you'll need:Experience: hands-on experience or relevant internships in Machine Learning or data science is preferredFoundational Knowledge: Strong academic or practical grounding in core machine learning concepts, statistics, and data structuresCoding & Frameworks: Proficiency in Python and familiarity with standard ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn) and SQLGrowth & Collaboration: Eagerness to learn from senior engineers, collaborate across product and engineering teams, and rapidly ramp up on MLOps best practicesTech Stack: Basic exposure to cloud platforms (like GCP) and data processing pipelines.

Conversational AI LeadON-SITE - UAEPermanent roleFinancial Services**Relocation benefits are on offer for those relocating from overseas**We are looking for an experienced AI leader to head the strategy, design and delivery of conversational AI initiatives across the organisation.This role will be responsible for transforming how customers and employees interact with the business through AI-powered chat, voice assistants, virtual agents and Generative AI. You will identify high-value use cases, define the conversational AI roadmap and lead initiatives from concept and proof of value through to enterprise-scale production.Business experience:10+ years industry experienceBanking or FS experienceLeadership & stakeholder managementProduct ownership and leadershipAct as an SME for Conversational AIDesign and run the roadmap for these initiativesEnsure Gen AI apps include appropriate controlsTechnical expertiseLarge Language Models and NLPSpeech-to-Text / Text-to-SpeechPrompt engineeringRAGML/OPS and EngineeringVector databases and semantic searchKnowledge graphsAI orchestrationCloud AI platformsEducation - MUST HAVE a degree to qualify for this role.**Only applicants with the relevant skillset against the above, will be contacted**

ABOUT THE ROLEYou will own the technical architecture of Acxiom's identity graph in MENAT and lead the data science team that works on marketing mix modelling (MMM) and High-Value Audience (HVA) solutions. The role sits between data engineering, data science and leadership. You design how identity data is resolved, governed and activated, then prove its commercial impact to clients.KEY RESPONSIBILITIESID graph architectureOwn the end-to-end design of the ID graph: ingestion, deterministic and probabilistic matching, identity resolution logic, match-rate and precision measurement, and refresh cadence.Define the data model, pipelines and cloud architecture (BigQuery, Snowflake) with data engineers; set standards for scalability, cost and latency.Build privacy-by-design into the graph: consent handling, anonymization/pseudonymization, clean-room integrations, and compliance with UAE PDPL, KSA PDPL and client policies.Evaluate and onboard data partners and match keys; decide build vs. partner vs. buy.Data science leadership (MMM & HVA)Lead delivery of MMM and multi-touch attribution work, including Bayesian MMM, calibration with incrementality tests, and budget optimization.Lead High-Value Audience modelling: propensity, lookalike, CLV and segmentation models activated through the ID graph.Set the team's methodology, code quality and model validation standards; move repeat work into reusable products.People & stakeholdersHire, coach and manage a team of data scientists; set goals and run performance reviews.Partner with client services, sales and product to scope solutions and support pre-sales.Present architecture decisions and model results to clients, the MD and the CEO in clear, decision-ready decks.MUST-HAVE QUALIFICATIONS10+ years in data science, data products or AI, including 3+ years leading teams.Hands-on depth in statistical and machine learning models: regression, Bayesian methods, time series, classification, clustering.Strong coding in SQL and Python; working knowledge of R.Production experience in big data environments such as BigQuery or Snowflake.Hands-on experience with identity resolution, customer data platforms or first-party data matching.Track record of working with data engineering and tech leads to ship data products.Proven delivery in media measurement: MMM, multi-touch attribution or incrementality testing.Strong media domain background: agency, adtech or data-provider experiencePowerPoint narratives for C-level audiences.