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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.

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

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.

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:

Senior Founding AI EngineerMusly Club | Full stack | Founding team | Full time | Remote - you do not have to be in Dubai/UAE (open to all countries)Musly is a private members’ club for people with Muslim roots, built around meaningful connections and experiences - digitally and IRL. We’re hiring a senior founding engineer to build musly club to the next level with us. You’ll work directly with our founder, fractional CTO, and another engineer. This is a hands-on role with ownership across product, full stack engineering, and AI.What you’ll buildSmarter member recommendations, AI chat, concierge, travel introductions, and event recommendations.The data and feedback loop connecting member intake, behavior, conversations, connections, events, and real-world outcomes.Smarter ai chatInternal tools that help Musly understand member demand, city trends, emerging segments, and what is working.Intelligence that helps our team find the right members for events and partner Events tools + platform with Ai seating arrangements and invitesexperiences, then learn from invitations, attendance, and feedback.The APIs, data pipelines, models, evaluations, and interfaces that make these products reliable at scale.Who you areYou’ve built and shipped products at an early-stage startup.You’re genuinely full stack and can own a feature from data model and backend through to the user experience.You can communicate clearly and effectively.You have deep hands-on experience with AI/ML and LLMs, including building production systems, evaluating their performance, and improving them with real data.You understand recommendations, ranking, or matching systems, or you’re ready to apply your ML experience to a network where both people need to find a connection valuable.You move fast, have excellent product judgment, and can turn an ambitious idea into something useful that ships.You’ve worked on a product available in multiple countriesYou’ve worked on a product and build for it to scale for over 500,000 usersYou want the responsibility and pace of a small founding team.What success looks likeIn your first 90 days, you’ll help turn data Musly already collects into a working intelligence system: better member recommendations, useful member understanding, smarter event invitations, and insights our team can act on. You’ll build the feedback loop that helps each of these improve as Musly grows.This is a chance to own a core part of Musly’s product and technical foundation as we scale the club across cities.Technical RequirementsAI and machine learningYou've shipped LLM features to production and kept them working: prompt design, structured outputs, tool use, retrieval (RAG) and embeddings.You've worked with more than one model provider (for example OpenAI and Anthropic, directly or through AWS Bedrock) and can manage cost, latency, rate limits and fallbacks.You build evaluations before you trust a system: labeled eval sets, offline metrics, LLM-as-judge with human calibration, and A/B tests.You understand recommendation, ranking or matching systems: candidate generation, ranking, cold start and exploration. Experience with two-sided (reciprocal) matching is a strong plus.You can learn from implicit feedback: event data, delayed outcomes, position bias and feedback loops that reinforce their own mistakes.You know when an LLM is the wrong tool. You've trained and shipped classical models (for example gradient boosting or logistic regression) on tabular data.You can design human-in-the-loop workflows, where reviewers correct model output and those corrections improve the system.Backend and dataStrong TypeScript and Node.js. You've built and operated Express (or similar) APIs in production.Deep MongoDB experience: schema design for document stores, indexing, aggregation pipelines and query performance. Experience with Atlas Vector Search or another vector store.Real-time systems: WebSockets or Socket.IO, and Redis for caching and pub/sub.Data pipelines and event tracking: you can design event schemas, run batch jobs and scheduled workloads, and build reliable data flows from product events to models.Frontend and mobileProduction React experience. React Native with Expo is a strong plus; if you're a strong React engineer, we expect you to pick it up quickly.Infrastructure and production qualityHands-on with AWS (EKS, S3, Lambda, CloudFront), Docker and Kubernetes. You don't need to be an SRE, but you can deploy, debug and scale what you build.Experience running services across multiple regions, including latency, data consistency and deployments.You write tested, reviewable code and care about observability. You've been on call and handled production incidents.Privacy and securityExperience handling sensitive personal data, including private messages and profile data.Working knowledge of GDPR.You design AI features with safety in mind: guardrails, moderation and limits on what data goes to third-party models.Nice to haveYou've built for a community, social, dating or membership product where both sides of a connection need to find it valuable.Experience with events or real-world experiences: invitations, capacity, seating, attendance.Stripe subscriptions, Twilio, OneSignal or other messaging and notification tooling.Next.js and Tailwind for internal tools and admin dashboards.Product analytics with Mixpanel or similar, including experiment design.Localization, time zones and data residency across countries.You use AI coding tools well and can set good practices for a small team.Answer the following question in your applicationWhat's something you built or fixed outside of work, even if it's small or simple?

We are looking for an AI Platform Operations Lead to take full ownership of our client's multi-cloud AI ecosystem. In this role, you’ll sit at the absolute bleeding edge of technology—architecting multi-agent orchestrations, scaling Voice AI, operationalizing Model Context Protocol (MCP), and running high-performance LLMOps across Azure OpenAI, AWS Bedrock, and Core42 Compass.What You’ll DoAgentic AI & MCP: Scale complex multi-agent orchestrations using LangGraph & Microsoft Agent Framework while operationalizing Model Context Protocol (MCP) integrations.LLMOps & Observability: Optimize PTU capacity, latency, token spend, and monitoring via Comet Opik, Azure Monitor, and AWS CloudWatch.Cloud & Voice Infrastructure: Manage AKS, Docker, APIM, and IaC (Terraform/Bicep) alongside cutting-edge Voice AI (ElevenLabs).Governance & Security: Enforce Responsible AI guardrails, content filtering, Entra ID RBAC, and zero-trust security.What We’re Looking ForDeep expertise in Azure AI, AWS Bedrock, Kubernetes, and Python.Proven track record in LLMOps, agentic workflows, and cloud-native automation.What's on offer?Competitive monthly salaryTax-free compensation package in DubaiVisa sponsorship and comprehensive benefitsHigh-visibility role shaping multi-market strategic initiatives