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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.

AI Engineer / Forward Deployed Engineer - AI & Agentic SystemsAbout the RoleDomain Rewiring means redesigning the operational domain itself — workflows, decision points, data flows and human roles — so that agentic AI becomes embedded into how work actually gets done.The Forward Deployed Engineering team works directly inside government entities to identify, build and deploy these systems, ensuring the underlying AI platform is shaped by real operational requirements rather than developed in isolation.Why JoinWork on complex applied AI problems. Re-engineer live government workflows and deploy agentic AI where operational decisions are actually made.Own outcomes, not just engineering. Define the users, workflow, success metrics and scope, then take the solution through to production and adoption.Small, senior teams. Work in compact squads of experienced engineers with significant autonomy and direct access to senior government stakeholders.Build the practice. Help establish the playbook for evaluation, AI-native software development and forward deployment, before helping grow the team around it.UAE-based opportunity. Relocation assistance is available for successful candidates and their families.The RoleWe are hiring a AI Engineer / Forward Deployed Engineer to join a small team of senior engineers responsible for taking complex government domains from discovery through to production AI systems.This is an individual contributor role first.You will personally own some of the organisation’s most challenging domain rewirings, working directly with users and senior stakeholders to understand how an operation works today and redesign how it should operate in an AI-native environment.There is no traditional business analyst, product manager or architect sitting between you and the problem. You define the users, workflow, scope, success metric and value case — and then build.You will own domain rewiring, adoption and realised business value, while partnering with specialists across AI engineering, product engineering, platform architecture and evaluation.As the programme grows, you may also lead a rewiring pod, hire engineers or own part of the domain portfolio. However, leadership sits alongside hands-on delivery rather than replacing it.What You OwnEmbed and define. Work directly inside government entities, understand how operations actually run and define the requirements yourself. Establish the metric, capture the baseline and agree it with the business owner before building.Design and ship. Architect agentic workflows spanning orchestration, retrieval, tools, agents, evaluation and human-in-the-loop processes — then write the production code.Navigate enterprise constraints. Take deployments through security, privacy, tenant isolation, governance and information-security reviews, creating reusable patterns for future deployments.Own adoption. Redesign the human process alongside the software. Success means changing how decisions are made, reducing cycle times, lowering costs or improving measurable outcomes.Prove value. Instrument products so their impact can be measured against an agreed baseline. Recommend scaling, redesigning or stopping initiatives based on evidence.Make the next deployment faster. Convert lessons from individual rewirings into reusable platform capabilities, engineering patterns and playbooks.First Three MonthsIndicative outcomes include:Delivering one lighthouse domain rewiring end-to-end within a major government entity, reaching production and demonstrating measurable improvement.Establishing an agreed value case and baseline with the relevant business owner and delivering the first value report.Contributing reusable capabilities to the underlying sovereign AI platform based on requirements discovered through real-world deployment.Who You AreStrong production engineer.You have personally shipped software end-to-end. Years of experience matter less than what you have successfully put into production.Field-hardened.You thrive in ambiguous, embedded and stakeholder-facing environments where the problem has not yet been clearly defined.Hands-on with agentic AI.You have practical depth across LLM and agent systems, including orchestration, retrieval, evaluation, agent design and prompt engineering. AI coding tools are part of your normal development workflow.Product owner by instinct.You are comfortable interviewing users, defining requirements, prioritising problems and cutting scope without waiting for a Product Manager.Business literate.You can discuss cost, cycle time, productivity, headcount and ROI with senior executives without hiding behind technical terminology.Enterprise literate.You understand identity, networking, data governance, security and approval processes required to move software into production within complex organisations.Value disciplined.You are willing to stop, redesign or descope your own work when the evidence shows it will not create sufficient value.Strongly PreferredForward Deployed Engineering, Solutions Engineering or technical delivery experience.Experience building or leading an FDE capability.Delivery experience within government, financial services or another regulated/high-assurance environment.Founder, founding engineer or zero-to-one product experience.Hands-on cloud and platform engineering experience, particularly Azure.Arabic and English capability, or experience operating effectively within bilingual environments.Evidence We Will Ask ForWe will want to understand:Two products you personally built that reached real users in production, including your contribution, scale and measurable outcome.For at least one product, the baseline before deployment, measured result afterwards, how the impact was measured and who validated it.One product you stopped, redesigned or significantly descoped because the expected value did not materialise, including the evidence behind that decision.Projected benefits in a business case are not considered realised impact.How Performance Is MeasuredPerformance is based on three primary outcomes:Speed: Time from problem definition to a rewired workflow being used in production.Value: Measurable impact against an agreed baseline, validated by the relevant business owner.Judgement: Rewirings that successfully pass the value checkpoint — and initiatives correctly stopped early when the evidence does not support further investment.Performance is not measured by lines of code, number of pilots or size of team.What This Role Is NotNot a management role disguised as engineering.You may lead engineers or a portfolio, but you remain personally responsible for delivering rewirings.Not advisory.Recommendations that end with a presentation or strategy document do not count. You build and ship.Not a pilot factory.A portfolio of proofs of concept that never reaches production or creates measurable value is not considered successful.

Company Description KON-ae is a next-generation AI Agent digital human workspace that enables enterprises to deploy real-time agentic AI avatars capable of performing tasks and collaborating with human teams. The company focuses on replacing fragmented tools and static automation with intelligent, always-on AI agents that communicate, act, and execute tasks through human-like avatar interfaces. KON-ae helps organizations eliminate operational friction, unlock contextual intelligence, and scale productivity without increasing headcount. Growing teams rely on KON-ae for AI agentic avatars, digital twin technology, customer support automation, internal query handling, knowledge base intelligence, and custom AI solutions. Founded in 2024 and based in Dubai, United Arab Emirates, KON-ae partners with forward-thinking enterprises to reimagine how work gets done through deeply integrated, interactive AI agents.Role Description The AI Engineer will design, build, and optimize AI agents and digital human avatars that integrate seamlessly into enterprise workflows. This full-time, on-site role in Dubai, United Arab Emirates involves developing and deploying machine learning models, including neural networks and NLP systems, to power real-time interactions and task execution across departments. The AI Engineer will collaborate with product, design, and engineering teams to translate business requirements into scalable AI solutions, implement APIs and integrations, and ensure robust performance in production environments. Day-to-day responsibilities include data analysis and pattern recognition, model training and evaluation, software development and testing, and continuous improvement of agent behavior and contextual intelligence. The role also includes documenting technical approaches, contributing to architecture decisions, and supporting enterprise customers during implementation and optimization phases.Qualifications Strong foundation in Computer Science and Software Development, including algorithms, data structures, and modern programming languages (e.g., Python, JavaScript, or Java).Hands-on experience with Neural Networks and Pattern Recognition, using frameworks such as TensorFlow, PyTorch, or similar deep learning libraries.Practical expertise in Natural Language Processing (NLP), including building, fine-tuning, and deploying language models for real-time conversational agents.Experience designing and deploying AI solutions in production environments, including RESTful APIs, microservices, and cloud platforms (e.g., AWS, GCP, Azure).Knowledge of data pipelines, model evaluation, and monitoring, with a focus on reliability, scalability, and low-latency inference.Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.Ability to work closely with cross-functional teams, communicate complex technical concepts clearly, and

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.

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.

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.