senior data engineer

ai engineer

data engineer

applied ai engineer

data analyst

data intelligence machine learning engineer

Dyson

<div>
 
 About Us
 
 
 
 At Dyson, we&rsquo;re driven by a relentless pursuit of innovation&mdash;pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson&rsquo;s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.
 
 
 You&rsquo;ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.
 
 
 
 About the role
 
 
 We are looking for a specialized Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
 </li>
 <li>
 Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
 </li>
 <li>
 Quality Assurance &amp; Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.
 </li>
 <li>
 Tooling &amp; Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
 </li>
 <li>
 Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.
 </li>
 <li>
 Set up and maintain robust data preparation infrastructure&mdash;optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.
 </li>
 <li>
 Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You&rsquo;ll help transform raw data into actionable insight, supporting both research and deployment.
 </li>
 <li>
 Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.
 
 
 
 </li>
 </ul>
 
 About you
 
 
 
 <ul>
 <li>
 At least 3+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.
 </li>
 <li>
 Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).
 </li>
 <li>
 Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).
 </li>
 <li>
 Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).
 </li>
 <li>
 Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.
 </li>
 <li>
 Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.
 </li>
 <li>
 Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.
 </li>
 <li>
 Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).
 </li>
 <li>
 Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).
 </li>
 <li>
 Strong background in feature engineering, data analysis, and data visualization&mdash;comfortable using tools like Jupyter, Tableau, or Power BI.
 </li>
 <li>
 Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.
 </li>
 <li>
 Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.
 </li>
 <li>
 Bachelor&rsquo;s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.
 
 
 
 </li>
 </ul>
 Dyson is an equal opportunity employer. We know that great minds don&rsquo;t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.
</div>

Data Intelligence Machine Learning Engineer

TALENTMATE

<div>
 
 Job Description
 
 
 
 
 About Us
 
 
 
 At Dyson, we&rsquo;re driven by a relentless pursuit of innovation&mdash;pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson&rsquo;s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.
 
 
 You&rsquo;ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.
 
 
 
 About the role
 
 
 We are looking for a specialized Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
 </li>
 <li>
 Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
 </li>
 <li>
 Quality Assurance &amp; Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.
 </li>
 <li>
 Tooling &amp; Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
 </li>
 <li>
 Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.
 </li>
 <li>
 Set up and maintain robust data preparation infrastructure&mdash;optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.
 </li>
 <li>
 Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You&rsquo;ll help transform raw data into actionable insight, supporting both research and deployment.
 </li>
 <li>
 Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.
 
 
 </li>
 </ul>
 
 About you
 
 
 
 <ul>
 <li>
 At least 3+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.
 </li>
 <li>
 Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).
 </li>
 <li>
 Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).
 </li>
 <li>
 Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).
 </li>
 <li>
 Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.
 </li>
 <li>
 Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.
 </li>
 <li>
 Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.
 </li>
 <li>
 Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).
 </li>
 <li>
 Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).
 </li>
 <li>
 Strong background in feature engineering, data analysis, and data visualization&mdash;comfortable using tools like Jupyter, Tableau, or Power BI.
 </li>
 <li>
 Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.
 </li>
 <li>
 Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.
 </li>
 <li>
 Bachelor&rsquo;s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.
 
 
 </li>
 </ul>
 Dyson is an equal opportunity employer. We know that great minds don&rsquo;t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.
 
 
 Job Details
 
 
 Role Level: Not Applicable Work Type: Full-Time Country: United Arab Emirates City: Dubai Company Website: http://careers.dyson.com Job Function: Engineering Company Industry/
 
 
 Sector: Appliances Electrical and Electronics Manufacturing
 
 
 What We Offer
 
 
 
 About The Company
 
 
 
 Searching, interviewing and hiring are all part of the professional life. The TALENTMATE Portal idea is to fill and help professionals doing one of them by bringing together the requisites under One Roof. Whether you're hunting for your Next Job Opportunity or Looking for Potential Employers, we're here to lend you a Helping Hand.
 
 
 Report
 
 
 Disclaimer: talentmate.com is only a platform to bring jobseekers &amp; employers together. Applicants are advised to research the bonafides of the prospective employer independently. We do NOT endorse any requests for money payments and strictly advice against sharing personal or bank related information. We also recommend you visit Security Advice for more information. If you suspect any fraud or malpractice, email us at abuse@talentmate.com.
</div>

Jorofy Private Limited

We are seeking a highly skilled Senior AI Engineer (PhD) to lead the design, development, and deployment of advanced Artificial Intelligence and Machine Learning solutions. The ideal candidate will possess a PhD in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or a related field, along with extensive industry experience in developing AI-driven products, conducting research, and translating innovative concepts into scalable enterprise solutions.The role requires expertise in Generative AI, Large Language Models (LLMs), Deep Learning, Natural Language Processing (NLP), Computer Vision, and MLOps practices.Key ResponsibilitiesAI Research &amp; Innovation<ul><li>Conduct advanced research in Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.</li><li>Evaluate emerging AI technologies, frameworks, and methodologies to enhance organizational capabilities.</li><li>Publish research findings, technical papers, patents, or contribute to industry thought leadership initiatives.</li><li>Lead proof-of-concept (PoC) development for innovative AI solutions.</li></ul>Solution Design &amp; Development<ul><li>Architect and develop scalable AI/ML models and intelligent systems.</li><li>Design and optimize Large Language Model (LLM)-based applications and AI agents.</li><li>Build and fine-tune machine learning and deep learning models for production environments.</li><li>Develop AI-powered applications involving NLP, Computer Vision, Predictive Analytics, and Recommendation Systems.</li><li>Integrate AI solutions with enterprise platforms, APIs, and cloud environments.</li></ul>Generative AI &amp; LLM Engineering<ul><li>Develop and optimize Retrieval-Augmented Generation (RAG) architectures.</li><li>Fine-tune foundation models and implement prompt engineering strategies.</li><li>Design AI agents, autonomous workflows, and conversational AI solutions.</li><li>Implement vector databases and semantic search frameworks.</li><li>Evaluate model performance, accuracy, safety, and responsible AI practices.</li></ul>MLOps &amp; Deployment<ul><li>Build end-to-end ML pipelines for model training, deployment, monitoring, and maintenance.</li><li>Implement CI/CD processes for AI applications.</li><li>Optimize model performance, scalability, and infrastructure costs.</li><li>Monitor model drift and ensure continuous improvement.</li></ul>Leadership &amp; Collaboration<ul><li>Provide technical leadership and mentorship to AI Engineers, Data Scientists, and ML Engineers.</li><li>Collaborate with business stakeholders to identify AI opportunities and define solution roadmaps.</li><li>Participate in strategic technology planning and AI transformation initiatives.</li><li>Review code, architecture, and technical deliverables to ensure quality standards.</li></ul>Required QualificationsEducation<ul><li>PhD in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Mathematics, Statistics, or a related discipline.</li></ul>Experience<ul><li>8+ years of experience in AI/ML development, including research and production deployment.</li><li>Proven experience leading AI projects in enterprise environments.</li><li>Experience working with Generative AI, LLMs, and advanced deep learning architectures.</li></ul>Technical SkillsProgramming<ul><li>Python (Advanced)</li><li>SQL</li><li>Java, C++, or Scala (Preferred)</li></ul>AI/ML Frameworks<ul><li>PyTorch</li><li>TensorFlow</li><li>Keras</li><li>Scikit-learn</li><li>Hugging Face Transformers</li></ul>Generative AI &amp; LLM Technologies<ul><li>OpenAI APIs</li><li>LangChain</li><li>LangGraph</li><li>LlamaIndex</li><li>RAG Architectures</li><li>Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS)</li></ul>Cloud Platforms<ul><li>AWS</li><li>Microsoft Azure</li><li>Google Cloud Platform (GCP)</li></ul>MLOps &amp; DevOps<ul><li>Docker</li><li>Kubernetes</li><li>MLflow</li><li>Kubeflow</li><li>Jenkins</li><li>GitHub Actions</li></ul>Databases<ul><li>PostgreSQL</li><li>MongoDB</li><li>Vector Databases</li></ul>Preferred Qualifications<ul><li>Publications in reputed journals or AI conferences (NeurIPS, ICML, ICLR, ACL, CVPR, etc.).</li><li>Experience in Agentic AI, Autonomous Systems, and Multi-Agent Frameworks.</li><li>Knowledge of AI Governance, Responsible AI, Explainable AI (XAI), and AI Security.</li><li>Industry certifications in Cloud, AI, or Machine Learning technologies.</li></ul>Core Competencies<ul><li>Strategic Thinking</li><li>Research &amp; Innovation</li><li>Problem Solving</li><li>Leadership &amp; Team Mentoring</li><li>Stakeholder Management</li><li>Communication &amp; Presentation Skills</li><li>Analytical Thinking</li><li>Project Management</li></ul>Key Deliverables<ul><li>Production-ready AI and Generative AI solutions.</li><li>Scalable machine learning pipelines and architectures.</li><li>AI research initiatives and innovation roadmaps.</li><li>Performance optimization and continuous model improvement.</li><li>Technical mentorship and knowledge-sharing across teams.</li></ul>Experience Required: 8&ndash;15+ Years Education: PhD (Mandatory) Level: Senior / Lead Individual Contributor or Technical Lead Role.Education:<ul><li>Master's (Preferred)</li></ul>Experience:<ul><li>Senior Engineer: 10 years (Required)</li></ul>Work Location: In person

Senior AI Engineer (PhD)

Dezzex

Who we are?Dezzex uses Artificial Intelligence and Machine Learning technologies to advance the motor insurance industry by creating a system to automate claims. We use these technologies to improve insurance processes, reduce operational costs and enable a seamless customer experience. Our solution offering is also designed for the insurance companies, Insurance companies, Smart cities application.For applying as a Sr. Machine Learning Engineer, the person should have overall 7-8 years of experience out of which 4-5 years of experience should be in Machine &amp; Deep Machine learning. Especially using Convolution Neural Network (CNN), Image Analytics TensorFlow, Open CV etc.For applying as just a data scientist (Junior), the person needs to have experience of 3 - 9 years.With our fast-growing data scientist's team, we are looking to hire self-motivated smart individuals who are willing to take challenges in the field of Al image &amp; video analytics &amp; machine learning.Responsibilities:The Machine &amp; Deep Machine Learning Software Engineer (Expertise in Computer Vision) will be an early member of a growing team with responsibilities for designing and developing highly scalable machine learning solutions that impact many areas of our business.The individual in this role will help in the design and development of Neural Network (especially Convolution Neural Networks) &amp; ML solutions based on our reference architecture which is underpinned by big data &amp; cloud technology, micro-service architecture and high performing compute infrastructure.Typical daily activities include contributing to all phases of algorithm development including ideation, prototyping, design, and development production implementation.Required Skills:An ideal candidate will have background in software engineering and data science with expertise in machine learning algorithms, statistical analysis tools, and distributed systems.Experience in building machine learning applications, and broad knowledge of machine learning APIs, tools, and open source librariesStrong coding skills and fundamentals in data structures, predictive modelling and big data concepts Experience in designing full stack ML solutions in a distributed compute environmentExperience working with Python, Tensor Flow, Kera's, Sci-kit, pandas, NumPy, AZURE, AWS GPU Excellent communication skills with multiple levels of the organizationPay: AED10,000.00 - AED20,000.00 per monthExperience:<ul><li>ML: 3 years (Required)</li><li>Development: 5 years (Required)</li></ul>Language:<ul><li>Arabic (Preferred)</li></ul>Work Location: In person

Sr. Machine learning engineer.

DATAMAZE . AI

<div>
 Hyderabad, India / Dubai, UAE
 
 
 Full-time
 
 
 Apply to Job
 
 
 
 About The Role
 
 
 
 We are looking for a highly motivated Machine Learning Engineer / Data Scientist to join our dynamic team. In this mid-level role, you will leverage your expertise in data science and machine learning to design, build, and deploy innovative models and solutions that address real-world challenges.
 
 
 You will collaborate with cross-functional teams, including data engineers, product managers, and software developers, to turn complex datasets into actionable insights and build machine learning solutions that drive business value. This is a fantastic opportunity to work on impactful projects, expand your skill set, and contribute to cutting-edge advancements in AI and data science.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Model Development: Design, train, and evaluate machine learning models to solve business problems such as classification, regression, clustering, and recommendation.
 </li>
 <li>
 Data Preparation: Work with large and complex datasets, performing data cleaning, preprocessing, and feature engineering to optimize model performance.
 </li>
 <li>
 Model Deployment: Deploy machine learning models into production environments, ensuring scalability and robustness.
 </li>
 <li>
 Algorithm Selection: Research and implement state-of-the-art algorithms and methodologies to enhance model accuracy and efficiency.
 </li>
 <li>
 Collaboration: Collaborate with data engineering teams to build data pipelines and ensure efficient data flow for machine learning workflows.
 </li>
 <li>
 Visualization and Reporting: Create clear and actionable visualizations and reports to communicate findings and results to stakeholders.
 </li>
 <li>
 Monitoring and Maintenance: Monitor model performance in production, address drift or bias issues, and optimize models as needed.
 </li>
 <li>
 Tool Development: Build tools and frameworks to enable rapid experimentation and iteration of machine learning models.
 </li>
 <li>
 Documentation: Maintain comprehensive documentation for models, experiments, and processes.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 Education:
 
 
 
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent experience).
 
 
 
 Technical Skills
 
 
 
 <ul>
 <li>
 Strong programming skills in Python, R, or similar languages.
 </li>
 <li>
 Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras.
 </li>
 <li>
 Proficiency in data manipulation and analysis using tools like Pandas, NumPy, and SQL.
 </li>
 <li>
 Experience with big data technologies such as Spark, Hadoop, or similar.
 </li>
 <li>
 Knowledge of cloud platforms and services (e.g., AWS SageMaker, Google AI Platform, Azure ML).
 </li>
 <li>
 Familiarity with MLOps practices and tools for CI/CD in machine learning workflows.
 </li>
 <li>
 Understanding of data visualization tools like Matplotlib, Seaborn, or Tableau.
 </li>
 <li>
 Strong grasp of statistical methods, probability, and optimization techniques.
 
 
 </li>
 </ul>
 
 Experience
 
 
 
 <ul>
 <li>
 3&ndash;5 years of experience in machine learning, data science, or a related field.
 </li>
 <li>
 Proven experience building and deploying machine learning models in production.
 </li>
 <li>
 Experience with natural language processing (NLP), computer vision, or time-series analysis is a plus.
 
 
 </li>
 </ul>
 
 Soft Skills
 
 
 
 <ul>
 <li>
 Strong problem-solving and analytical thinking abilities.
 </li>
 <li>
 Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
 </li>
 <li>
 Ability to work independently and collaboratively within a team.
 </li>
 <li>
 Curiosity and eagerness to stay updated on the latest advancements in machine learning and AI.
 
 
 </li>
 </ul>
 
 About The Company
 
 
 
 Datamaze is a dynamic company specializing in AI, Data, and Analytics consulting services. We are dedicated to transforming businesses by leveraging the power of data and artificial intelligence. With a team of industry experts in technology, business strategy, and data science, Datamaze offers a range of services including data strategy development, AI model creation, advanced analytics, and data visualization. Our approach is client-centric, focusing on creating customized solutions that address unique business challenges and objectives. The team, comprising data scientists, AI experts, strategists, and engineers, is committed to continuous learning and innovation.
 
 
 Datamaze positions itself as a trusted partner for businesses looking to navigate the complex data and AI landscape, aiming to optimize operations, inform decision-making, and maintain a competitive edge.
 
 
 Apply Now
</div>

Machine Learning Engineer / Data Scientist (Mid-Level)

INNOBAYT INNOVATIVE SOLUTIONS

About the RoleInnobayt is seeking a highly motivated AI Engineer Intern for a 3-month intensive internship. During this program, you'll gain hands-on experience working on real-world AI projects involving Machine Learning, Large Language Models (LLMs), AI Agents, Retrieval-Augmented Generation (RAG), and Computer Vision. You'll collaborate closely with our experienced engineering team to develop innovative and impactful AI solutions.Key Responsibilities<ul><li>Assist in developing, fine-tuning, and optimizing machine learning models.</li><li>Build AI-powered solutions, including AI Agents and LLM-based applications.</li><li>Prepare and preprocess datasets for AI model development.</li><li>Research and experiment with LLMs, RAG, prompt engineering, and emerging AI technologies.</li><li>Integrate AI models into applications, APIs, and production workflows.</li><li>Evaluate, test, and improve AI model performance.</li><li>Collaborate with the engineering team on real-world AI projects.</li><li>Document technical work, experiments, and project progress.</li></ul>What We're Looking for Technical Skills<ul><li>Bachelor's degree (or final-year student) in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field. Fresh graduates are encouraged to apply.</li><li>Proficiency in Python and familiarity with AI/ML frameworks such as TensorFlow, PyTorch, or Hugging Face.</li><li>Basic understanding of Machine Learning, Deep Learning, Natural Language Processing (NLP), or Computer Vision.</li><li>Familiarity with LLMs, embeddings, vector databases, RAG, LangChain, or OpenAI APIs is an advantage.</li><li>Basic knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP) is a plus.</li></ul>Language Requirement<ul><li>Fluent in Arabic (required)</li><li>Fluent in English (required)</li></ul>Mindset &amp; Work Approach<ul><li>Passion for Artificial Intelligence and emerging technologies.</li><li>Eagerness to learn, experiment, and solve real-world challenges.</li><li>Strong analytical, problem-solving, and communication skills.</li><li>Ability to work effectively in a collaborative and fast-paced environment.</li></ul>What We Offer<ul><li>Hands-on experience working on real-world AI projects.</li><li>Mentorship from experienced AI and software engineers.</li><li>Exposure to modern AI tools, frameworks, and industry best practices.</li><li>A nominal monthly stipend (the exact amount will be discussed during the interview).</li><li>Internship certificate upon successful completion.</li><li>Opportunity for a full-time role based on performance.</li></ul>Job Type: InternshipWork Location: In person

AI Engineer Intern – 3-Month Internship

Ministry of Economy & Tourism

<div>
 
 
 الهدف العام للوظيفة
 
 
 
 خبير في الذكاء الاصطناعي ضمن قسم الذكاء الاصطناعي في الوزارة، يتولى ضمان نشر أنظمة الذكاء الاصطناعي بصورة فعّالة وأخلاقية وقابلة للتوسع عبر عمليات الوزارة. يجسّر هذا الدور الفجوة بين القدرات التقنية للذكاء الاصطناعي والاحتياجات المؤسسية الحيوية، من خلال الإشراف البشري المتمحور حول الإنسان وحوكمة الأنظمة وتحسينها المستمر. يجمع هذا المنصب بين العمق التقني ومعرفة الأعمال وإدارة أصحاب المصلحة وقيادة التغيير لضمان استدامة تطبيقات الذكاء الاصطناعي وتحقيقها لقيمة مؤسسية قابلة للقياس
 
 
 
 المسؤوليات الرئيسية
 
 
 <ul>
 <li>
 تحديد متطلبات البيانات الجاهزة للذكاء الاصطناعي والإشراف عليها بما يشمل بيانات التدريب وبيانات الإدخال التشغيلية ونماذج القرار، وضمان جودتها واتساقها.
 </li>
 <li>
 تصميم وإدارة سير عمل الإشراف البشري-الآلي (Human-in-the-Loop) للتحقق من مخرجات الذكاء الاصطناعي قبل النشر التشغيلي.
 </li>
 <li>
 مراقبة دقة نماذج الذكاء الاصطناعي ومستويات الثقة والكشف عن انحراف الأداء للحفاظ على الفاعلية التشغيلية وضمان الاستمرارية.
 </li>
 <li>
 اختبار وكلاء الذكاء الاصطناعي وتدريبهم من خلال آليات التغذية الراجعة وهندسة الطلبات وبروتوكولات التعلم الخاضع للإشراف.
 </li>
 <li>
 تطوير وتطبيق أطر حوكمة الذكاء الاصطناعي والمبادئ التوجيهية الأخلاقية وسياسات الامتثال المتوافقة مع اللوائح الحكومية المعتمدة.
 </li>
 <li>
 المشاركة في تصنيف تطبيقات الذكاء الاصطناعي حسب مستوى مخاطرها وتأثيرها، وإرساء معايير رقابة متدرجة تتناسب مع درجة خطورة كل تطبيق.
 </li>
 <li>
 قيادة الإشراف على الموردين وحوكمة العقود لضمان السلطة التقنية والشفافية ومنع الاحتكار التقني من قِبل الموردين.
 </li>
 <li>
 تحديد مخاطر الذكاء الاصطناعي والتخفيف منها بما تشمل عدم موضوعية النموذج في نتائجه، ومحاولات التلاعب بالنموذج، وإفساد بيانات التدريب، وتراجع أداء النماذج بمرور الوقت.
 </li>
 <li>
 تصميم وقيادة برامج التثقيف في الذكاء الاصطناعي للمطورين والمستخدمين وأصحاب المصلحة المتأثرين بتطبيقات الذكاء الاصطناعي عبر الوزارة.
 </li>
 <li>
 إرشاد فرق العمل المشتركة وتنسيق التعاون متعدد التخصصات بين فرق تطوير الحلول والهندسة والتشغيل والأعمال.
 </li>
 <li>
 ترجمة القدرات والقيود التقنية لأنظمة الذكاء الاصطناعي إلى توصيات عملية وتوجيهات واضحة تُمكّن الإدارة وأصحاب المصلحة من اتخاذ قرارات مدروسة.
 </li>
 <li>
 تحديد مؤشرات أداء نماذج الذكاء الاصطناعي ومتابعتها بما تشمل نسبة الدقة، ودرجة يقين النموذج، ومعدلات قبول المخرجات، ومؤشرات انحراف الأداء، وإعداد التقارير الدورية.
 </li>
 <li>
 إدارة حلقات التغذية الراجعة من مستخدمي الذكاء الاصطناعي والفرق التشغيلية لتحسين النماذج والعمليات باستمرار.
 </li>
 <li>
 تحديد حالات استخدام الذكاء الاصطناعي الناشئة والتوصية بتحسينات على سير العمل والواجهات لتحقيق أقصى قيمة مؤسسية.
 </li>
 <li>
 دعم التوسع في نشر تطبيقات الذكاء الاصطناعي عبر الوزارة من خلال توحيد المعايير، وبناء قوالب وأدوات جاهزة قابلة لإعادة الاستخدام، وتوثيق أفضل الممارسات لضمان الاتساق والكفاءة.
 </li>
 <li>
 التعامل مع عزوف الموظفين وترددهم في تبني الذكاء الاصطناعي من خلال ربط تطبيقاته بواقع عمل كل فريق، وبناء الثقة، وتعزيز الانفتاح لتسريع تبني الحلول الذكية.
 </li>
 <li>
 بناء شبكات تبادل المعرفة والخبرات في مجال الذكاء الاصطناعي عبر الجهات الحكومية، وتعزيز التعاون ومشاركة أفضل الممارسات بين الكوادر المتخصصة.
 </li>
 <li>
 التحقق من مخرجات نماذج الذكاء الاصطناعي وتحديد الحالات الحدية والاستثنائية، وتطبيق الحكم البشري المدروس في القرارات المعقدة لضمان موثوقية الأنظمة ودقتها.
 </li>
 <li>
 تنفيذ أي مهام أخرى يكلف بها رئيس القسم في نطاق اختصاصه.
 </li>
 </ul>
 
 
 المؤهلات والخبرة
 
 
 <ul>
 <li>
 دكتوراه - علوم الحاسب / الذكاء الاصطناعي / هندسة / إحصاء أو ما يعادلها (بدون خبرة)
 </li>
 <li>
 ماجستير - علوم الحاسب / الذكاء الاصطناعي / علوم البيانات / هندسة أو ما يعادلها (3 سنوات خبرة)
 </li>
 <li>
 بكالوريوس - علوم الحاسب / الذكاء الاصطناعي / هندسة الحاسوب / إحصاء أو ما يعادلها (7 سنوات خبرة)
 </li>
 </ul>
 
 
 الشهادات المهنية
 
 
 <ul>
 <li>
 شهادة معتمدة في الذكاء الاصطناعي وتعلم الآلة على المنصات السحابية مثل Google Cloud Professional ML Engineer / AWS Machine Learning Specialty / Microsoft Azure AI Engineer Associate
 </li>
 <li>
 شهادة في نماذج اللغة الكبيرة وهندسة الطلبات مثل DeepLearning.AI Generative AI / Prompt Engineering Specialization
 </li>
 <li>
 شهادة في حوكمة الذكاء الاصطناعي وأخلاقياته مثل IAPP AI Governance Professional / ISO 42001 Lead Implementer)
 </li>
 <li>
 شهادة في علوم البيانات وهندستها مثل IBM Data Science Professional / Databricks Data Engineer Associate)
 </li>
 <li>
 شهادة في تشغيل نماذج الذكاء الاصطناعي مثل MLflow MLOps Certified / Kubeflow Practitioner / DataRobot MLOps
 </li>
 <li>
 شهادة في أمن الذكاء الاصطناعي والهجمات المعادية مثل Certified AI Security Professional / OWASP AI Security
 </li>
 <li>
 شهادة في إدارة المشاريع وقيادة التغيير أو ما يعادلها PMP / PRINCE2 / Prosci Change Management
 </li>
 <li>
 دورة في تصميم سير العمل الإنساني-الآلي (Human-in-the-Loop) واختبار النماذج وتقييم الانجراف
 </li>
 <li>
 دورة في بنية بيانات الذكاء الاصطناعي ورسم خرائط المعرفة (Knowledge Graphs / Data Mapping)
 </li>
 <li>
 دورة في قياس أداء نماذج الذكاء الاصطناعي ومؤشرات الدقة ومعدلات القبول
 </li>
 <li>
 دورة في أساسيات أمن المعلومات وحماية البيانات في بيئات الذكاء الاصطناعي الحكومية
 </li>
 </ul>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Job Purpose
 
 
 
 Serve as an Artificial Intelligence (AI) expert within the Ministry's AI Department, responsible for ensuring the effective, ethical, and scalable deployment of AI systems across the Ministry's operations. This role bridges the gap between AI technical capabilities and critical business needs through human-centered oversight, AI governance, and continuous optimization. The position combines deep technical expertise with business knowledge, stakeholder management, and change leadership to ensure AI applications remain sustainable and deliver measurable organizational value.
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Define and oversee AI-ready data requirements, including training datasets, operational input data, and decision models, while ensuring data quality, consistency, and integrity.
 </li>
 <li>
 Design and manage Human-in-the-Loop (HITL) workflows to validate AI outputs before operational deployment.
 </li>
 <li>
 Monitor AI model accuracy, confidence levels, and performance drift to maintain operational effectiveness and ensure service continuity.
 </li>
 <li>
 Test and train AI agents using feedback mechanisms, prompt engineering, and supervised learning protocols.
 </li>
 <li>
 Develop and implement AI governance frameworks, ethical guidelines, and compliance policies aligned with approved government regulations.
 </li>
 <li>
 Participate in classifying AI applications according to their risk and impact levels and establish proportionate control measures based on the level of risk.
 </li>
 <li>
 Lead vendor oversight and contract governance to ensure technical ownership, transparency, and prevent vendor lock-in.
 </li>
 <li>
 Identify and mitigate AI-related risks, including model bias, adversarial attacks, training data poisoning, and model performance degradation over time.
 </li>
 <li>
 Design and lead AI awareness and education programs for developers, end users, and stakeholders affected by AI implementations across the Ministry.
 </li>
 <li>
 Guide cross-functional teams and coordinate multidisciplinary collaboration among solution development, engineering, operations, and business teams.
 </li>
 <li>
 Translate the technical capabilities and limitations of AI systems into practical recommendations and clear guidance to support informed decision-making by management and stakeholders.
 </li>
 <li>
 Define and monitor AI model performance metrics, including accuracy rates, confidence scores, output acceptance rates, and model drift indicators, and prepare periodic performance reports.
 </li>
 <li>
 Manage feedback loops from AI users and operational teams to continuously improve AI models and business processes.
 </li>
 <li>
 Identify emerging AI use cases and recommend workflow and user interface enhancements to maximize organizational value.
 </li>
 <li>
 Support the enterprise-wide scaling of AI applications by standardizing practices, developing reusable templates and tools, and documenting best practices to ensure consistency and efficiency.
 </li>
 <li>
 Address employee resistance and hesitation toward AI adoption by aligning AI solutions with team-specific business needs, building trust, and promoting openness to accelerate the adoption of intelligent solutions.
 </li>
 <li>
 Build knowledge-sharing networks and AI communities of practice across government entities, fostering collaboration and the exchange of best practices among AI professionals.
 </li>
 <li>
 Validate AI model outputs, identify edge cases and exceptional scenarios, and apply sound human judgment in complex decision-making to ensure the reliability and accuracy of AI systems.
 </li>
 <li>
 Perform any other duties assigned by the Head of the AI Department within the scope of the role.
 </li>
 </ul>
 
 
 Qualifications &amp; Experience
 
 
 <ul>
 <li>
 PhD in Computer Science, Artificial Intelligence, Engineering, Statistics, or a related field (no experience required).
 </li>
 <li>
 Master's Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field (minimum 3 years of relevant experience).
 </li>
 <li>
 Bachelor's Degree in Computer Science, Artificial Intelligence, Computer Engineering, Statistics, or a related field (minimum 7 years of relevant experience).
 </li>
 </ul>
 
 
 Professional Certifications
 
 
 <ul>
 <li>
 Professional certification in Artificial Intelligence and Machine Learning on cloud platforms, such as
 
 Google Cloud Professional Machine Learning Engineer
 
 ,
 
 AWS Machine Learning Specialty
 
 , or
 
 Microsoft Azure AI Engineer Associate
 
 .
 </li>
 <li>
 Certification in Large Language Models (LLMs) and Prompt Engineering, such as
 
 DeepLearning.AI Generative AI Specialization
 
 or
 
 Prompt Engineering Specialization
 
 .
 </li>
 <li>
 Certification in AI Governance and Ethics, such as
 
 IAPP AI Governance Professional (AIGP)
 
 or
 
 ISO/IEC 42001 Lead Implementer
 
 .
 </li>
 <li>
 Certification in Data Science and Data Engineering, such as
 
 IBM Data Science Professional Certificate
 
 or
 
 Databricks Data Engineer Associate
 
 .
 </li>
 <li>
 Certification in AI Operations (MLOps), such as
 
 MLflow MLOps Certified
 
 ,
 
 Kubeflow Practitioner
 
 , or
 
 DataRobot MLOps
 
 .
 </li>
 <li>
 Certification in AI Security and Adversarial AI, such as
 
 Certified AI Security Professional
 
 or
 
 OWASP AI Security
 
 .
 </li>
 <li>
 Certification in Project Management and Change Leadership, such as
 
 PMP
 
 ,
 
 PRINCE2
 
 , or
 
 Prosci Change Management
 
 .
 </li>
 <li>
 Training course in
 
 Human-in-the-Loop (HITL)
 
 workflow design, AI model testing, and model drift evaluation.
 </li>
 <li>
 Training course in AI data architecture, data mapping, and knowledge graphs.
 </li>
 <li>
 Training course in AI model performance measurement, including accuracy metrics, confidence scores, and output acceptance rates.
 </li>
 <li>
 Training course in information security fundamentals and data protection in government AI environments.
 </li>
 </ul>
 
 
 
</div>

خبير الذكاء الاصطناعي - Artificial Intelligence Expert

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