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About the Role:We are looking for an ambitious and adaptable AI Software Engineer to help build our trading-intelligence platform. The product is a website plus a Windows desktop app. The public site is the landing page. Users download the app and run the product on their own machine. You will work with the engineer who owns the platform, across the full stack, and turn business needs into working software.This is a product-engineering role. You will use modern LLM APIs inside a live trading product. We are hiring early-career engineers: strong graduates, or people with about one to two years of work.What you will work on:Web: A Next.js and React site, covering the public landing page and the signed-in product for pre-trade analysis, trade review, and chat.Backend: Python services for market analysis, user accounts, and live broker data.Chat: A trading assistant that answers questions, uses market and account context, and streams a clear written reply.Desktop: An Electron app, shipped as a Windows executable, that users download from the site and use as their main way into the product.Markets: MetaTrader 5 for prices, charts, and account history across forex, stocks, commodities, crypto, and indices.Infrastructure: The live system on AWS, deployed and looked after from the Linux command line.Key Responsibilities:Platform work: Improve the product wherever the work is: backend logic, the web app, the chatbot, or the desktop app.Backend: Build and maintain Python services for analysis, market data, authentication, and MT5.Chat: Help shape the assistant: how it understands a question, which tools it calls, and how it writes the answer.Desktop: Connect the desktop app to the backend, including authentication, updates, and safe use of model APIs.Trading integration: Integrate MT5 for prices, charts, account history, and position sizing. Treat live accounts with care, and change order flow only when that behaviour is explicitly in scope.Deployment: Help deploy and operate the stack on AWS EC2, including services, logs, and safe restarts.AI development tools: Use tools such as Cursor to move faster, and review what they produce before it ships.Collaboration: Work directly with the engineer who owns the platform and with the business. Explain trade-offs in plain language and ship small, testable changes.Experience:About one to two years in a software role, or a strong graduate with projects that stand in for that time. An internship counts.You have built something with a web frontend and a Python backend, at work, in an internship, or as a substantial project. You can read an existing codebase, follow how one service calls another, and make a small change without breaking the rest.You have called a hosted LLM from your own code. A course project, a side project, or a feature at work is enough. You know what a prompt, a tool call, and a streamed reply are, and you have seen a model answer badly and fixed the code around it.You are comfortable in a terminal. You do not need to have run production. You should be ready to deploy on a Linux server with someone who already knows the setup.A working interest in markets is enough. You should know what MT5 is, what a symbol and a timeframe are, and why account size matters for a trade. Brokerage experience is not required.Hard Skills & Qualifications:Architecture: You can look at a small system and explain where data lives, which part talks to the outside world, and what the website and the desktop app each do.Backend: Solid Python. Familiar with FastAPI, async code, Postgres, and Redis, from work or from projects you can talk through.Frontend: Hands-on React and Next.js with TypeScript. Familiar with Tailwind. You can build a screen and match an existing design.Desktop: You have used Electron, or a similar desktop stack, and you can learn how we package the Windows app.AI integration: You have used a hosted model API such as OpenAI, Gemini, or OpenRouter. You can change a chatbot’s behaviour in code, not only by rewriting a prompt.Markets: A basic grasp of forex, indices, commodities, and crypto, plus the idea of risk per trade. You understand why a trading assistant must not invent prices or place trades by accident.AWS and Linux: You can use the Linux command line. You have some exposure to cloud servers, or you can get comfortable on EC2 quickly with guidance.AI tools: You already use coding assistants in daily work, and you read the change before you keep it.Communication: You can explain a technical choice to someone who is not an engineer, and you can turn a business request into a small piece of working software.Nice-to-Haves:A small Electron app you can show.A chatbot that calls tools, even as a project.A deploy you did yourself, on any Linux host.Personal trading, a markets elective, or time around a broker platform.How the role works:You will pair with the engineer who already owns the platform. Expect to pick up a slice of work, such as a chatbot change, an analysis fix, a desktop update, or a screen in the app, and carry it through with review. You will learn the trading domain and the production setup on the job. Judgment still matters. Trading users act on what the product says..

Every day, millions of shipments move around the world on data that nobody is looking at in real time. Orders, ASNs, invoices, and tracking events flow between 3PLs, forwarders, brands, and retailers, then get flattened into a status that tells you something went wrong only after it already has.SwiftB2B exists to change that. We are an AI operating system for global supply chains. One platform where 3PLs, freight forwarders, brands, retailers, and manufacturers can see every item, shipment, and exception as it happens. The platform does not stop at flagging a delay. It works out what the delay costs, weighs the options, and starts the fix.We built it on 15 years of SwiftAnt’s work in supply chain and integration technology, and on a team that has spent careers in logistics, retail, and enterprise software. We are a Databricks partner and our data platform runs on the lakehouse.A demo that passes and a system that answers are different things - we know that anyone can show a chatbot answering “which shipments are late?” on a good day. The hard part is making it answer from governed metrics only, refusing when it should, and proving a pass rate before a customer ever sees it. This role is a lot about that. You will make “ask the platform anything” true rather than aspirational, and build the agents that detect an exception, decide what it costs, and escalate rather than guess.What you will doOwn the natural-language query architecture on Databricks: Genie, the semantic layer as ground truth, and the guardrail layer that makes sure natural language resolves against governed metrics and never against free-form SQL on raw tables.Build and run the evaluation harness. Fifty questions per industry pack, drawn from the KPI catalogue, with expected answers, at or above ninety per cent before anything is demonstrated or deployed. Publish the pass rate, good or bad.Try to break your own guardrail, and fix what you find. When a question fails, work out whether the fix belongs in the prompt or in the metric definition. Usually it is the definition.Build agent workflows on the platform: alerting, narrative generation, exception triage, corrective actions. Agents that escalate when uncertain, with a decision trace someone can re-examine later.Put a number on every query: tokens, latency, cost per answer. Keep a technology watch on models and decide when a rule beats a model.Teach analysts and customers what the system can and cannot answer, so nobody demos below the gate, whatever the commercial pressure.Work with our analytics engineers on the semantic layer and with our solution engineers on getting agents into the product behind a human gate.What we are looking forReal experience shipping LLM-based features to production, not notebooks. RAG, structured output, tool use, guardrails.Strong Python, strong SQL, and comfort on Databricks. Experience with Genie, Mosaic AI, or Unity Catalog is a strong plus. The Databricks Generative AI Engineer Associate certification is nice to have.You have built an evaluation set and measured a pass rate, and you can talk honestly about what it did not catch.Experience with agent frameworks (Claude Agent SDK, LangGraph, Semantic Kernel, or similar) and with designing the boundary an agent operates inside.Prompt engineering at expert depth. In most roles it is a starting competence. Here it is the craft.You understand that a semantic layer or metric definition is the source of truth, and you have worked with one.A background in supply chain, logistics, or operations data is an advantage. Knowing what OTIF or a dwell time actually means makes your evaluation questions better.A degree in Computer Science, Data Science, or a related field, or equivalent practical experience.Not sure you tick every box?Apply anyway. Nobody we have hired matched the list line for line. If you have put an LLM feature in front of real users and measured whether it was right, we want to talk to you. Tell us about the time your eval harness caught something, or the time it should have. That tells us more than a polished CV. Equal opportunitySwiftB2B hires on skill and curiosity. We do not discriminate on the basis of race, religion, nationality, gender, sexual orientation, age, disability, or any other protected characteristic. If you need an adjustment at any stage of the process, tell us and we will make it happen.

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.

Job Description: We are seeking a skilled AI Engineer to design, develop, and implement artificial intelligence and machine learning solutions. You will work with technical and business teams to build intelligent systems, optimize AI models, and support the integration of AI technologies into business applications.Responsibilities:Develop, train, and deploy machine learning and AI models.Collect, preprocess, and analyze data for AI and machine learning applications.Design and implement AI solutions based on business and technical requirements.Evaluate model performance and optimize algorithms for accuracy and efficiency.Integrate AI models and services into existing software applications and systems.Collaborate with software engineers, data scientists, and other technical teams.Research emerging AI technologies and recommend improvements to existing solutions.Requirements:Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field preferred.Previous experience in AI engineering, machine learning, or software development is an advantage.Strong programming skills in Python and familiarity with relevant AI/ML frameworks.Understanding of machine learning algorithms, data structures, and model evaluation techniques.Familiarity with cloud platforms, APIs, databases, and deployment tools is a plus.Strong analytical, problem-solving, and communication skills.Ability to work independently, manage multiple projects, and adapt to rapidly evolving technologies.Benefits:Competitive salary and performance-based bonuses.Supportive work environment with opportunities for technical growth.Access to AI training, certifications, and professional development programs.Exposure to innovative AI technologies and real-world projects.Work-life balance with flexible or hybrid scheduling options.

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

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