Your AI career in the UAE starts here

We have partnered with a leading diversified group with a global footprint to hire a Head of AI. The Group is moving AI from ambition to execution across multiple businesses, and this person will lead that shift. You will own AI strategy, build the team and the platform, and deliver production systems that move commercial numbers. The remit is Group-wide, the mandate is clear and the opportunity to build from the ground up is real.This role suits a technical AI leader who has taken systems into production at scale and can sit equally comfortably in an engineering review and a boardroom.About the role:Define and lead the Group's AI strategy, identifying and prioritising high-value use cases across business unitsBuild and lead a cross-functional AI organisation spanning engineering, data science and MLOpsOwn architecture and delivery of production AI systems, including GenAI, LLM applications, agentic workflows and traditional MLEstablish governance, security and responsible AI standards that the Group can trustPartner with business leaders to drive adoption and measure ROI against clear commercial outcomesEvaluate and select AI platforms, vendors and partners, and keep the Group ahead of a fast-moving market About you:10+ years in AI, machine learning or data science, with at least 3 years leading teamsProven record of deploying AI and ML systems into production at enterprise scaleHands-on understanding of LLMs, retrieval and orchestration frameworks, MLOps and cloud AI platformsExperience building an AI function or Centre of Excellence from scratchCommercially minded, able to connect AI investment to revenue, cost and customer outcomesCredible with executives, regulators and technical teams alike

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.

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

Landmark DigitalAs a part of the Landmark Group, a renowned retail and hospitality conglomerate in the Middle East, North Africa, and India, Landmark Digital is the dynamic digital arm of Landmark Retail, serving as the cornerstone of our omnichannel business strategy.Headquartered in Dubai, UAE, we oversee the digital operations of eight leading brands across diverse geographies, with ambitious plans for expansion into new territories and functions. Joining us means becoming a vital part of the Middle East's most significant bricks-to-clicks success story, boasting an impressive year-on-year growth rate exceeding 100%.Comprising a talented workforce of over 700 professionals across diverse domains, Landmark Digital spearheads various functions including Enterprise and E-commerce Tech, Product Management, User Design, and MarTech, among others. With our futuristic outlook, we are committed to delivering seamless digital experiences to our customers.Job Specification - We are looking for a Lead AI Architect to lead the design and architecture of AI products, from problem definition and technical discovery through production delivery and continuous improvement. You will turn business and product goals into secure, scalable, measurable AI solutions and help teams choose the right approach across a fast-changing AI landscape.You will work closely with product owners, enterprise architects, engineering, data, security and operations teams. This is a hands-on technical leadership role: you will own solution architecture, validate critical design choices through prototypes and reference implementations, and guide teams through delivery.Key Focus Areas:Partner with product owners to define AI use cases, user journeys, feasibility, business outcomes and acceptance criteria. Challenge when conventional software or analytics is a better fit than AI.Lead end-to-end AI product architecture across experience, application, model, data, integration and infrastructure layers. Document decisions, trade-offs, dependencies and non-functional requirements.Design appropriate solutions using predictive ML, generative AI, retrieval-augmented generation (RAG), multimodal models and agentic workflows. Use autonomous or multi-agent designs only where they add value.Work with enterprise architects to align solutions with target architectures, integration patterns, platform standards and governance. Build reusable reference architectures and components without duplicating enterprise capabilities.Evaluate models, platforms, frameworks and vendors through structured experiments. Recommend build-versus-buy decisions based on quality, security, latency, total cost, portability and operating needs.Guide engineering teams through implementation, architecture and code reviews, integration and production readiness. Prototype high-risk assumptions and mentor engineers and other architects.Embed responsible AI and security by design: privacy, access controls, permission-aware retrieval, tenant isolation, prompt-injection defenses, safe tool use, audit trails and human approval for high-impact actions.Define evaluation, testing, monitoring and lifecycle controls for models, prompts, retrieval and agents. Plan fallbacks, failure handling, rollbacks and incident ownership with platform and operations teams.Track the AI landscape and translate developments into practical roadmaps and guidance. Communicate decisions clearly to technical teams, product owners and senior stakeholders.Additional experienceRegulated or enterprise environments; AI platform/CoE design; Model Context Protocol (MCP) and other tool-integration patterns; knowledge graphs; model-serving and open-weight deployment; domain-specific AI validation; architecture or cloud certifications; mentoring across multiple product teams. Specific frameworks and certifications are advantages, not substitutes for delivery evidence.Knowledge, Skills & Experience Relevant Job ExperienceA strong track record in software, solution or AI architecture, with evidence of shipping and operating enterprise-grade products. Indicative experience: 8+ years in engineering/architecture, including 3+ years working on AI/ML solutions; equivalent demonstrated experience is welcome.Direct experience taking an LLM-based or agentic product into production, beyond demos and proofs of concept. Ability to explain design choices, evaluation results, operating costs and lessons from real failures.Broad understanding of the AI landscape: foundation and open-weight models, conventional ML, RAG, embeddings, retrieval/reranking, context engineering, fine-tuning, multimodal systems and agent orchestration. Sound judgment about when each is appropriate.Hands-on ability to prototype and review production code, preferably in Python and at least one product/backend stack. Strong API, distributed-system, data-pipeline and enterprise integration design skills.Experience with at least one major cloud and its AI services, plus containerized or managed deployment, CI/CD, infrastructure automation, MLOps/LLMOps and observability.Practical experience with AI evaluation, grounding quality, safety testing, model/prompt versioning and cost/latency optimization. Familiarity with identity, least privilege, data boundaries and secure tool/API access.Ability to influence without relying on reporting authority, resolve architectural trade-offs and work effectively with enterprise architects, engineering teams and product owners.Clear written and verbal communication: architecture diagrams, decision records, delivery guidance and explanations suitable for business audiences.Degree in computer science, engineering or a related field, or equivalent practical experience.What Success Looks LikeProduct goals become clear, agreed architectures and measurable delivery/evaluation criteria.AI products meet agreed quality, safety, reliability, latency and cost targets in production.Teams reuse approved patterns, and architecture decisions remain aligned with enterprise standards.Product owners and engineers can make faster, better-informed trade-offs as the AI landscape changes.

We are looking for a technically strong and delivery-focused AI Project Specialist to design, develop, and deliver AI solutions for the organization and manage the projects that take them from concept to production use. Reporting to the Group PMO, you will work with business teams to identify and scope AI use cases, build and deploy solutions end to end, and coordinate stakeholders and external vendors to ensure timely delivery and adoption. The ideal candidate should combine strong technical depth in machine learning and AI engineering with hands-on experience building solutions end to end, and the ability to scope, plan, and deliver projects with business stakeholders and external vendors.Qualifications and Technical Skills:Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related quantitative fieldMaster’s degree in Data Science, Artificial Intelligence, Machine Learning, or a similar scientific discipline is preferred. Years & nature of experience 3–5 years of experience developing and deploying AI or machine-learning solutions in production environments.Demonstrated experience delivering solutions end to end, from problem definition and data preparation through model or application development, deployment, and user adoption. Experience managing or coordinating technical projects with cross-functional stakeholders and external vendors. • Strong programming skills in Python and SQL, with sound software-engineering practice (version control, testing, API design). • Solid grounding in machine learning, including supervised and unsupervised learning, feature engineering, model evaluation and validation, and common ML libraries and frameworks. • Hands-on experience with generative AI and LLMs, including building applications on large language models, prompt engineering, retrieval augmented generation, agent frameworks, evaluation, and guardrails. Strong data skills, including data modeling, data pipelines, relational databases, and integration with enterprise systems (CRM, ERP) through APIs. Deployment and MLOps experience, including containerization, cloud platforms (Azure, AWS, or Google Cloud), model versioning, monitoring, and CI/CD. Experience with workflow-automation and system-integration tools. Project management skills covering scoping, planning, risk, and vendor management, with familiarity with agile delivery and PMO practices. Experience with cloud AI services (e.g., Azure AI, AWS Bedrock, Google Vertex AI) and vector databases is an advantage. Experience in regulated or service-oriented environments such as government, free zones, healthcare, or financial services is preferred. Project management certification (PMP, PRINCE2, or equivalent) and Arabic language skills are an advantage.

General Summary:The Sr. Digital Delivery Specialist at EMSTEEL designs, builds and delivers advanced AI and machine-learning solutions that create measurable business value across the Group’s steel and building-materials operations. The role sits within the Digital Centre of Excellence and focuses on Generative AI, Agentic AI, Computer Vision, AI Digital Twins and productionizing models through modern MLOps practices applied to industrial use cases such as process optimization and predictive maintenance. Working end-to-end from use-case discovery and experimentation to deployment, monitoring and adoption. Essential Duties and Responsibilities:Partner with business stakeholders and SMEs to identify, qualify and prioritize high-impact AI/ML use cases aligned to EMSTEEL’s strategic objectives.Design, develop and deploy Generative AI solutions (LLM-based assistants, RAG pipelines, document intelligence, summarization and content generation) tailored to enterprise needs.Build Agentic AI systems — autonomous and multi-agent workflows that reason, plan, use tools and orchestrate tasks, including Agent-to-Agent (A2A) collaboration and protocols across enterprise applications.Deliver industrial AI use cases across the value chain — process optimization, predictive maintenance, quality prediction, energy and yield optimization, and anomaly detection for steel and building-materials operations.Develop and operationalize AI Digital Twin solutions that simulate, monitor and optimize plant assets and production processes in real time.Build Computer Vision solutions for defect detection, surface-quality inspection, safety monitoring and process automation on the shop floor.Develop, validate and optimize classical and deep-learning models for prediction, optimization, anomaly detection and process control.Implement robust MLOps practices: CI/CD for models, automated pipelines, feature stores, model registry, versioning, monitoring, drift detection and retraining.Engineer scalable data and AI pipelines on Databricks and integrate solutions across the Microsoft (Azure) stack and Dataiku.Own end-to-end AI project delivery — from proof-of-concept to production — ensuring quality, security, performance and on-time delivery.Drive AI adoption by embedding solutions in downstream applications, updating SOPs, enabling users and delivering relevant training and change management.Establish responsible-AI, governance, evaluation and guardrail practices for GenAI and agentic solutions (accuracy, safety, bias, data privacy and cost control).Communicate findings, model behavior and business impact clearly to both technical and non-technical audiences.Additional Duties and ResponsibilitiesContribute to the AI reference architecture, reusable components and internal best-practice standards.Stay current with emerging GenAI/agentic frameworks, foundation models and tooling, and pilot promising innovations.Mentor junior Sr. Digital Delivery Specialists and analysts and support a culture of experimentation and continuous learning.Knowledge, Skills and/or Abilities RequiredStrong hands-on experience in machine learning, deep learning and statistical modelling using Python (and SQL).Proven expertise in Generative AI LLMs, prompt engineering, RAG, embeddings, vector databases and fine-tuning.Experience building Agentic AI solutions and multi-agent / Agent-to-Agent (A2A) systems using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen or similar.Experience delivering industrial AI use cases process optimization, predictive maintenance and AI digital twins.Hands-on Computer Vision experience (OpenCV, PyTorch/TensorFlow, object detection, segmentation, defect/anomaly detection).Solid MLOps capability MLflow, model deployment, containerization with Docker/Kubernetes, CI/CD, GitHub / GitHub Actions and model monitoring.Proficiency with Databricks (Spark, Delta Lake, Unity Catalog, notebooks) for large-scale data and ML workloads.Working knowledge of the Microsoft/Azure AI stack (Azure Machine Learning, Azure OpenAI, Azure AI Foundry, Fabric/Synapse) and Dataiku.Ability to translate business problems into technical solutions and communicate results effectively.Strong problem-solving, stakeholder-management and cross-functional collaboration skills.Minimum Requirements (Must Have):EducationBachelor’s / Master’s in Computer Science, Data Science, AI, Statistics, Engineering, Physics or Mathematics.Experience4+ years in data science / machine learning delivery.Demonstrated delivery of GenAI or agentic solutions to production.2+ years implementing cutting-edge AI/GenAI technologies.Exposure to manufacturing, heavy industry or steel operations.Experience with responsible-AI governance and LLMOps.Training / CertificationsCertifications in Machine Learning / AI, Databricks, Microsoft Azure AI or Dataiku.