Your AI career in the UAE starts here

At Why Hiring, we believe in the power of connecting talented individuals with incredible remote job opportunities. Our mission is to simplify the job search process and empower professionals to find fulfilling roles that align with their skills and passions, regardless of geographical constraints.About The Role:This position requires strong quantitative skills, experience with large and complex datasets, and the ability to collaborate with analysts, engineers, and mission partners to develop high-impact solutions that drive intelligence insight and operational advantage.Perform statistical analysis and data exploration to extract meaningful insights from complex datasetsSupport the development and validation of predictive models to identify trends and patternsContribute to the development of AI/ML models and data-driven solutionsConduct exploratory data analysis to support decision-making and operational planningAssist in building and maintaining data pipelines for data collection, processing, and integrationCollaborate with data scientists, engineers, and cross-functional teams to improve data workflowsDevelop scripts and tools to automate repetitive tasks and improve efficiencySupport initiatives to enhance data quality, structure, and accessibilityApply basic experimental and analytical approaches to test ideas and evaluate outcomesContribute to improving data systems, workflows, and analytical capabilitiesAssist in identifying data gaps, inconsistencies, and opportunities for optimizationParticipate in team meetings, project discussions, and knowledge-sharing sessionsEducationBachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.Required SkillsProficiency in Python, R, SQL, or similar programming languages.Experience with data visualization platforms such as Power BI or Tableau.Strong understanding of statistical techniques and modeling approaches.Foundational knowledge of AI/ML concepts and applications.Experience working with relational and/or NoSQL databases.Familiarity with cloud-based big data technologies.Ability to explain complex technical findings to non-technical audiences.Understanding of intelligence analysis processes and mission requirements.Experience working with structured and unstructured datasets in complex environments.ExperienceMinimum of 0 to 2 years of experience in data science, analytics, machine learning, or related technical fields.Why Hiring does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.

Company Description BPS is an international multi-brand value-added distributor of software and cloud technologies, solutions, and services, enabling resellers across 16 countries to build and grow their business through a dedicated marketplace. The company partners with leading global information technology providers and enhances their offerings with its own professional services and advisory capabilities. BPS serves diverse IT channel partners, including IT resellers, Managed Service Providers/CSPs, system integrators, and telecom/datacenter operators. Known for strong expertise in technology, licensing, and go-to-market strategies, BPS offers a collaborative environment for professionals who want to work at the intersection of cutting-edge IT and business growth.Role Description: About the RoleWe are seeking a highly motivated and technically skilled AI Solution Architect / AI Consultant to join our team. This role is ideal for an AI enthusiast with hands-on experience across multiple AI platforms, Large Language Models (LLMs), AI agents, and enterprise AI deployments.The successful candidate will be responsible for helping customers understand, adopt, and maximize the value of AI technologies. This includes designing AI solutions, building AI agents, conducting demonstrations and technical workshops, delivering customer training, and staying ahead of the rapidly evolving AI landscape.Key ResponsibilitiesDesign, develop, and deploy AI-powered solutions using modern AI platforms and technologies.Conduct technical workshops, training sessions, and enablement programs for partners and their customers.Lead technical AI discussions with customers and stakeholders, translating business requirements into AI solutions.Create proof-of-concepts (POCs), demonstrations, and prototypes showcasing AI capabilities.Collaborate with sales, presales, and engineering teams to develop customer-focused AI strategies.Produce technical documentation, solution architectures, training materials, and implementation guides.Support customer AI adoption initiatives and drive successful project outcomes.Required QualificationsBachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related field.2+ years of experience in AI, Machine Learning, Data Science, or related technologies.Strong understanding of Generative AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Prompt Engineering, and AI Agents.Experience working with AI platforms such as: Microsoft COPILOTKnowledge in designing and deploying AI applicationsStrong presentation and customer engagement skills.Excellent verbal and written communication skills in English.Preferred SkillsExperience with Microsoft Copilot, Copilot Studio, Azure AI Foundry, and AI Agent development.Knowledge of AI security, governance, compliance, and responsible AI practices.Experience integrating AI solutions with business applications and enterprise systems.Certifications in AI, Cloud, or Data platforms are a plus.Ability to quickly learn emerging technologies and effectively communicate complex concepts to both technical and business audiences.What Makes You SuccessfulPassionate about AI innovation and continuous learning.Customer-focused with strong consulting and communication skills.Comfortable speaking in front of technical and executive audiences.Able to simplify complex AI concepts into practical business value.Self-driven, proactive, and adaptable in a fast-paced technology environment.Why Join UsWork with cutting-edge AI technologies and industry-leading platforms.Engage with customers across various industries to solve real business challenges.Access continuous learning and certification opportunities.Be part of a team driving AI transformation and innovation.

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.

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 have a new opportunity for "AI Specialist" with our client. If interested then please send me your updated CV toJob Title: AI SpecialistLocation: DubaiDuration: PermanentQualificationsBachelor’s degree in computer science, Engineering, Mathematics, or a related quantitative field; master’s degree in data science, Artificial Intelligence, Machine Learning, or a similar scientific discipline is preferred.Years & nature of experience3–5 years of experience developing and deploying AI or machine-learning solutions in production environments.Core CompetenciesTechnical Skills• 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 buildingapplications on large language models, prompt engineering, retrievalaugmented 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.Interested candidates send me your CV with below details.Expected salary:Notice Period:Current Location:Citizen / Visa Status / Work Permit: