civil engineer

mechanical engineer

electrical engineer

sales engineer

planning engineer

machine learning engineer

ADGM

Maseera&rsquo;s AI and Big Data Center of Excellence covers the range of Data and AI skills, from Strategy, Governance, Data Science, Data Architecture, AI Engineering and Visual Insights. When combined with Maseera&rsquo;s broader Strategy and professional practice, we are bringing together the unique ability to drive end-to-end business change through the application of Data and AI. You'll learn, grow and advance in an innovative culture that thrives on shared success, diverse ways of thinking and enables boundaryless opportunities that can drive your career in new and exciting ways.If you&rsquo;re looking for a challenging career while working in a vibrant environment with access to training and a global network of experts, this could be the role for you. As part of our global team, you'll be based in Abu Dhabi, and you&rsquo;ll be working with cutting-edge technologies, and you will have the opportunity to develop a wide range of new skills on the job.In our team you will:<ul><li>Be at the forefront of the Fintech industry, and you&rsquo;ll create, own and make it a reality for our subsidiaries looking to better serve their customers.</li><li>Become an integral part of our Data and AI team with the credibility, expertise and dependable insights.</li><li>Develop your skills with our world class training programmes.</li><li>Drive and implement real world solutions into production that drive business value across our company.</li><li>Work alongside global teams to collaborate and deliver the best solutions for our customers.</li><li>Drive your career progression via our structured career development programs.</li></ul>In this role you will:<ul><li>Solve challenging business problems using advanced machine learning methods such as Deep Learning and quantitative analytics.</li><li>Understand business requirements and support the development of business cases.</li><li>Run discovery analytics to identify new and innovative opportunities.</li><li>Partner with developers and engineers to deploy machine learning algorithms to deliver business value.</li><li>Define approaches to embed and scale machine learning models.</li><li>Build reusable assets and solutions and develop best practices for current and future business problems.</li><li>Communicate and provide guidance to senior leadership and teams.</li><li>Contribute data science expertise to new enterprise activities.</li><li>Shape DS Strategy and the delivery of DS solutions.</li><li>Operate across Headquarter and multiple subsidiaries.</li><li>Develop others by bringing your unique knowledge and skill sets to the wider practice.</li></ul>We are looking for:<ul><li>B.Sc./Master/PhD in computer science, data science, AI or relevant field</li><li>Relevant work experience in data science, machine learning, and business analytics</li><li>Practical experience in coding languages e.g. Python, R, Scala, etc.; (Python preferred)</li><li>Proficiency in database technologies e.g. SQL, ETL, No-SQL, DW, and Big Data technologies e.g. pySpark, Hive, etc.</li><li>Experienced working with structured and also unstructured data e.g. Text, PDFs, jpgs, call recordings, video, etc.</li><li>Knowledge of machine learning modelling techniques and how to fine-tune those models e.g. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc.</li><li>Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, hugging face, etc.</li><li>Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge.</li><li>Experience of using Cloud technologies e.g. AWS, GCP or Azure.</li><li>Specialised visualisation techniques e.g. D3.js, ggplot etc.</li><li>Strong verbal/written communication &amp; data presentation skills</li><li>Awareness of Large Language Models (LLMs) and a strong enthusiasm for continuous learning and staying updated with the latest advancements in the field</li></ul>Set yourself apart:<ul><li>Willingness and ability to learn quickly and apply creative thinking to finding great solutions and drive their implementation</li><li>Ability to successfully build relationships with customers and stakeholders</li><li>Mastery of problem solving and solutioning</li><li>Proven history and background in Fintech Industry</li></ul>Job Type: Full-timePay: From AED25,000.00 per month

Data Scientist

New York University Abu Dhabi

<div>
 The Modern Compilers Lab in the Computer Science program at New York University Abu Dhabi, seeks to recruit a research assistant to work on the intersection of compilers and deep learning.
 
 
 Many companies, such as Google, Facebook, and Amazon are building new specialized programming frameworks. This is because these companies need to allow their users to write simple, high-level code and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures without changing the code.
 
 
 Our research team at NYUAD (New York University Abu Dhabi) is developing a new programming framework called Tiramisu [1]. Unlike existing frameworks, Tiramisu can perform advanced code optimizations that are hard to apply otherwise. Because of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines).
 
 
 In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel), enhancing data locality by fusion, and blocking (i.e. accessing arrays in a way that improves temporal and spatial data locality). A large number of optimizations exist and choosing which optimization should be used and which should not is important for performance. In some situations, some optimizations are harmful to performance while they are beneficial in other situations. Currently, there is no way to help users choose which optimizations should be used. Expert programmers usually spend a lot of time trying different optimizations manually to find the best set of optimizations.
 
 
 The goal of this project is to add support for automatic code optimization in Tiramisu. In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module that relies on machine learning has been developed and we want to take that module to the next level. The final product of this project would be a compiler pass that allows Tiramisu to automatically choose which optimization should be used for a given unoptimized program. We want to produce a high-quality technique that can be used by the users of Tiramisu and especially by our partner companies and research labs.
 
 
 Candidates must hold a bachelor&rsquo;s degree or equivalent in Computer Science, though a master&rsquo;s degree is preferred. The ideal candidate will have Internship or experience in the areas of compilers and artificial intelligence.
 
 
 For consideration, applicants need to submit a cover letter, curriculum vitae, transcript, statement of research interests and two letters of reference, all in PDF format. If you have any questions, please email Prof. Riyadh Baghdadi at baghdadi@nyu.edu or mrasras@nyu.edu.
 
 
 The terms of employment are very competitive and include housing and educational subsidies for children. Applications will be accepted immediately and candidates will be considered until the position is filled.
 
 
 
 About NYU Abu Dhabi
 
 
 
 https://nyuad.nyu.edu/en/
 
 
 NYU Abu Dhabi is the first comprehensive liberal arts and research campus in the Middle East to be operated abroad by a major American research university. Times Higher Education ranks NYU among the top 30 universities in the world, making NYU Abu Dhabi the highest-ranked university in the UAE and MENA region. NYU Abu Dhabi has integrated a highly selective undergraduate curriculum across the disciplines with a world center for advanced research and scholarship. The university enables its students in the sciences, engineering, social sciences, humanities, and arts to succeed in an increasingly interdependent world and advance cooperation and progress on humanity&rsquo;s shared challenges. NYU Abu Dhabi&rsquo;s high-achieving students have come from over 120 countries and speak over 100 languages. Together, NYU's campuses in New York, Abu Dhabi, and Shanghai form the backbone of a unique global university, giving faculty and students opportunities to experience varied learning environments and immersion in other cultures at one or more of the numerous study-abroad sites NYU maintains on six continents.
 
 
 NYUAD is committed to upholding a culture of non-discrimination, anti-harassment, dignity, and mutual respect; providing equal access and opportunity; and fostering academic excellence in learning, research, and teaching.
 
 
 UAE Nationals are encouraged to apply.
 
 
 References
 
 
 [1] http://tiramisu-compiler.org/
 
 
 
 Equal Employment Opportunity Statement
 
 
 
 
 For people in the EU, click here for information on your privacy rights under GDPR: www.nyu.edu/it/gdpr
 
 
 
 NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. All interested persons are encouraged to apply for vacant positions at all levels.
 
 
 
 Sustainability Statement
 
 
 
 NYU aims to be among the greenest urban campuses in the country and carbon neutral by 2040. Learn more at nyu.edu/sustainability
</div>

Junior Research Scientist in the Division of Science (Computer Science) – Dr. Riyadh Baghdadi

Space42

<div>
 
 Overview
 
 
 
 
 ABOUT US
 
 
 
 Space42 (ADX: SPACE42) is a UAE-based AI-powered SpaceTech company that integrates satellite communications, geospatial analytics and artificial intelligence capabilities to enlighten the Earth from space. Established in 2024 following the successful merger between Bayanat and Yahsat, Space42&rsquo;s global reach allows it to address the rapidly evolving needs of its customers in governments, enterprises, and communities.
 
 
 Our vision is to pioneer beyond today for humanity to experience a better tomorrow. Space42 challenges traditional approaches with advanced AI and cutting-edge satellite technology, making space more accessible and redefining how data from space can be used on Earth. We aim to achieve this by connecting people to rewire potential, informing decisions to reimagine impact and enabling action to redefine tomorrow.
 
 
 For more information visit: www.space42.ai, follow us on X and Instagram @Space42ai
 
 
 
 Role Summary
 
 
 
 As a Senior AI Engineer &ndash; Geospatial Intelligence &amp; Advanced AI, you will lead the design, implementation, and operationalization of production-grade AI capabilities that turn multi-source geospatial data into decision-ready intelligence. You will work closely with Product Managers, Data Engineers, Software Developers, and domain experts to deliver scalable, reliable solutions across geospatial computer vision (e.g., satellite/remote-sensing imagery), graph/topology-aware modeling (networks and relational geospatial structures), optimization, and anomaly detection.
 
 
 &ldquo;Advanced AI&rdquo; in this role is an accelerator: you will selectively apply modern AI patterns (including multimodal/foundation-model capabilities where appropriate) to improve analyst workflows, automation, and interpretability&mdash;while keeping GEOINT accuracy, traceability, and operational reliability as the primary success criteria.
 
 
 You will be hands-on while also providing technical leadership&mdash;driving best practices for model development, ML system architecture, MLOps, reliability engineering, and responsible deployment.
 
 
 
 Responsibilities
 
 
 
 
 Technical leadership &amp; delivery
 
 
 
 <ul>
 <li>
 Lead end-to-end delivery of ML/AI solutions from problem framing and prototyping to production deployment, monitoring, and continuous improvement.
 </li>
 <li>
 Provide technical direction on model selection, experimentation strategy, evaluation methodology, and architecture.
 </li>
 <li>
 Mentor and review work of AI Engineers; raise engineering standards through code reviews, design reviews, and reusable patterns.
 
 
 </li>
 </ul>
 
 Geospatial intelligence (GEOINT) products
 
 
 
 <ul>
 <li>
 Build AI-powered geospatial intelligence workflows that transform imagery and geospatial data into actionable outputs (alerts, change maps, object inventories, situational overlays).
 </li>
 <li>
 Partner with domain teams to define geospatial product requirements, acceptance criteria, and quality thresholds; translate them into model and pipeline requirements.
 </li>
 <li>
 Drive dataset strategy for GEOINT tasks (labeling guidelines, sampling, balancing, provenance, and ground-truth validation).
 </li>
 <li>
 Ensure model outputs are map-ready: georeferenced, interpretable, and compatible with cartographic and downstream GEOINT production pipelines (e.g., layers, metadata, confidence measures, and audit trails).
 
 
 </li>
 </ul>
 
 Model development &amp; experimentation (Geospatial Intelligence, Graph AI &amp; Advanced AI)
 
 
 
 <ul>
 <li>
 Design, train, tune, and validate Deep Learning and classical ML models for geospatial computer vision (e.g., object detection/segmentation/change detection on satellite imagery).
 </li>
 <li>
 Develop and evaluate Graph Neural Network (GNN) models and topological learning approaches for problems involving spatial relationships, networks, and infrastructure graphs.
 </li>
 <li>
 Apply graph-based learning for use cases such as transport networks, connectivity analysis, supply chains, and geospatial relationship modeling.
 </li>
 <li>
 Contribute to analyst productivity and decision support by integrating advanced AI patterns where they measurably help (e.g., multimodal models for triage, structured extraction, search/summarization over GEOINT artifacts), with clear evaluation, governance, and guardrails.
 </li>
 <li>
 Define and implement evaluation approaches for advanced AI components (offline test sets, human-in-the-loop review, red-teaming where relevant) and ensure outputs are reliable, attributable, and policy-compliant.
 </li>
 <li>
 Build robust feature engineering pipelines and evaluation frameworks, including task-specific metrics (e.g., mAP/IoU, geospatial accuracy), graph metrics, and GenAI quality measures.
 
 
 </li>
 </ul>
 
 Production ML &amp; MLOps
 
 
 
 <ul>
 <li>
 Build and launch models in production using best practices in CI/CD, model versioning, experiment tracking, and automated testing.
 </li>
 <li>
 Define and manage SLAs/SLOs for model services (latency, throughput, accuracy drift, availability).
 </li>
 <li>
 Implement monitoring for data quality, model drift, and operational health; drive incident triage and resolution.
 
 
 </li>
 </ul>
 
 Data &amp; platform collaboration
 
 
 
 <ul>
 <li>
 Collaborate with engineers, product managers, and analysts to understand business needs and data requirements.
 </li>
 <li>
 Partner with data infrastructure teams to improve dataset reliability, lineage, governance, and access patterns.
 </li>
 <li>
 Build and maintain production-grade data pipelines (extraction, transformation, loading) that support training and inference.
 
 
 </li>
 </ul>
 
 Operational excellence &amp; communication
 
 
 
 <ul>
 <li>
 Develop clear documentation for models, pipelines, evaluation artifacts, and operational runbooks.
 </li>
 <li>
 Communicate trade-offs and results to technical and non-technical stakeholders.
 </li>
 <li>
 Enjoy collaborating in a multicultural and distributed environment.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 Education
 
 
 
 <ul>
 <li>
 Master&rsquo;s degree in Computer Science, Statistics, Information Systems, or another quantitative field (PhD is a plus).
 
 
 </li>
 </ul>
 
 Experience
 
 
 
 <ul>
 <li>
 5+ years of experience in software engineering, data engineering, or ML engineering (or equivalent).
 </li>
 <li>
 3+ years of hands-on experience developing ML/AI algorithms and deploying them into production environments.
 </li>
 <li>
 Experience delivering GEOINT-grade analytics or products with quality controls (QA, traceability, auditability) and operational reliability expectations.
 
 
 </li>
 </ul>
 
 Technical competencies
 
 
 
 <ul>
 <li>
 Deep Learning frameworks (e.g., PyTorch, TensorFlow) and classical ML (e.g., scikit-learn, XGBoost).
 </li>
 <li>
 Geospatial intelligence &amp; GeoAI: raster/vector data fundamentals, map projections/CRS, tiling/geo-referencing, and quality considerations for intelligence products.
 </li>
 <li>
 Geospatial computer vision: detection/segmentation/classification, change detection, multi-spectral/SAR basics, and post-processing for geospatial products.
 </li>
 <li>
 Familiarity with geospatial tooling and data stacks (e.g., GDAL/rasterio, GeoPandas equivalents, PostGIS, cloud-optimized formats such as COG, STAC concepts, and OGC-style interoperability is a plus).
 </li>
 <li>
 Graph AI and topology-aware learning: experience with Graph Neural Networks (GNNs), graph embeddings, network representation learning, and modeling relational/topological structures.
 </li>
 <li>
 Familiarity with graph ML frameworks such as PyTorch Geometric, DGL, or similar libraries.
 </li>
 <li>
 Familiarity with modern AI patterns for augmenting workflows (e.g., embeddings/vector search, basic multimodal model integration) and how to evaluate them safely in production.
 </li>
 <li>
 ML system design: online/offline inference, batch scoring, model serving patterns, feature stores.
 </li>
 <li>
 Data engineering: big data pipelines, architectures and datasets, queuing and streaming processing.
 </li>
 <li>
 Strong programming skills in Python and at least one of Java / Scala / Go.
 </li>
 <li>
 Version control and collaboration tooling (GitHub, GitLab).
 
 
 </li>
 </ul>
 
 Ideally, you&rsquo;ll also need
 
 
 
 <ul>
 <li>
 Experience with geospatial / remote sensing / satellite imagery data.
 </li>
 <li>
 Highly scalable systems and data stores.
 </li>
 <li>
 Cloud platforms such as AWS or GCP (experience with managed ML services is a plus).
 </li>
 <li>
 Cartography experience (map design principles, symbology, labeling, scale/CRS considerations) and delivering analyst-friendly geospatial products.
 </li>
 <li>
 Photogrammetry / 3D reconstruction experience (e.g., SfM/MVS concepts, orthorectification, DEM/DSM generation, bundle adjustment fundamentals) and working with aerial/satellite imagery pipelines.
 </li>
 <li>
 Experience translating AI outputs into cartographic deliverables (e.g., map layers, annotations, styling rules, and uncertainty visualization).
 </li>
 <li>
 Familiarity with geospatial tooling and formats (e.g., raster/vector concepts, GDAL/rasterio equivalents), and deployment of geospatial analytics at scale.
 </li>
 <li>
 Experience with MLOps tooling (e.g., MLflow, Kubeflow, Airflow, DVC, Weights &amp; Biases).
 </li>
 <li>
 Containerization and orchestration (Docker, Kubernetes).
 </li>
 <li>
 LLM/VLM experience (nice-to-have): applying multimodal/LLM capabilities to GEOINT workflows (e.g., RAG over mission documentation, agentic tooling, structured extraction), plus practical evaluation and guardrails.
 </li>
 <li>
 Experience implementing GenAI safety patterns (PII handling, content filtering, jailbreak resistance), model governance, and security considerations.
 </li>
 <li>
 Experience building human-in-the-loop review workflows for GEOINT outputs (QA, adjudication, auditability) and operating under high-reliability expectations.
 </li>
 </ul>
</div>

Senior AI Engineer (Space42)

TALENTMATE

<div>
 
 Overview
 
 
 
 Job Description
 
 
 
 About Us
 
 
 
 Space42 (ADX: SPACE42) is a UAE-based AI-powered SpaceTech company that integrates satellite communications, geospatial analytics and artificial intelligence capabilities to enlighten the Earth from space. Established in 2024 following the successful merger between Bayanat and Yahsat, Space42&rsquo;s global reach allows it to address the rapidly evolving needs of its customers in governments, enterprises, and communities.
 
 
 Our vision is to pioneer beyond today for humanity to experience a better tomorrow. Space42 challenges traditional approaches with advanced AI and cutting-edge satellite technology, making space more accessible and redefining how data from space can be used on Earth. We aim to achieve this by connecting people to rewire potential, informing decisions to reimagine impact and enabling action to redefine tomorrow.
 
 
 For more information visit: www.space42.ai, follow us on X and Instagram @Space42ai
 
 
 
 Role Summary
 
 
 
 As a Senior AI Engineer &ndash; Geospatial Intelligence &amp; Advanced AI, you will lead the design, implementation, and operationalization of production-grade AI capabilities that turn multi-source geospatial data into decision-ready intelligence. You will work closely with Product Managers, Data Engineers, Software Developers, and domain experts to deliver scalable, reliable solutions across geospatial computer vision (e.g., satellite/remote-sensing imagery), graph/topology-aware modeling (networks and relational geospatial structures), optimization, and anomaly detection.
 
 
 &ldquo;Advanced AI&rdquo; in this role is an accelerator: you will selectively apply modern AI patterns (including multimodal/foundation-model capabilities where appropriate) to improve analyst workflows, automation, and interpretability&mdash;while keeping GEOINT accuracy, traceability, and operational reliability as the primary success criteria.
 
 
 You will be hands-on while also providing technical leadership&mdash;driving best practices for model development, ML system architecture, MLOps, reliability engineering, and responsible deployment.
 
 
 
 Responsibilities
 
 
 
 
 Technical leadership &amp; delivery
 
 
 
 <ul>
 <li>
 Lead end-to-end delivery of ML/AI solutions from problem framing and prototyping to production deployment, monitoring, and continuous improvement.
 </li>
 <li>
 Provide technical direction on model selection, experimentation strategy, evaluation methodology, and architecture.
 </li>
 <li>
 Mentor and review work of AI Engineers; raise engineering standards through code reviews, design reviews, and reusable patterns.
 
 
 </li>
 </ul>
 
 Geospatial intelligence (GEOINT) products
 
 
 
 <ul>
 <li>
 Build AI-powered geospatial intelligence workflows that transform imagery and geospatial data into actionable outputs (alerts, change maps, object inventories, situational overlays).
 </li>
 <li>
 Partner with domain teams to define geospatial product requirements, acceptance criteria, and quality thresholds; translate them into model and pipeline requirements.
 </li>
 <li>
 Drive dataset strategy for GEOINT tasks (labeling guidelines, sampling, balancing, provenance, and ground-truth validation).
 </li>
 <li>
 Ensure model outputs are map-ready: georeferenced, interpretable, and compatible with cartographic and downstream GEOINT production pipelines (e.g., layers, metadata, confidence measures, and audit trails).
 
 
 </li>
 </ul>
 
 Model development &amp; experimentation (Geospatial Intelligence, Graph AI &amp; Advanced AI)
 
 
 
 <ul>
 <li>
 Design, train, tune, and validate Deep Learning and classical ML models for geospatial computer vision (e.g., object detection/segmentation/change detection on satellite imagery).
 </li>
 <li>
 Develop and evaluate Graph Neural Network (GNN) models and topological learning approaches for problems involving spatial relationships, networks, and infrastructure graphs.
 </li>
 <li>
 Apply graph-based learning for use cases such as transport networks, connectivity analysis, supply chains, and geospatial relationship modeling.
 </li>
 <li>
 Contribute to analyst productivity and decision support by integrating advanced AI patterns where they measurably help (e.g., multimodal models for triage, structured extraction, search/summarization over GEOINT artifacts), with clear evaluation, governance, and guardrails.
 </li>
 <li>
 Define and implement evaluation approaches for advanced AI components (offline test sets, human-in-the-loop review, red-teaming where relevant) and ensure outputs are reliable, attributable, and policy-compliant.
 </li>
 <li>
 Build robust feature engineering pipelines and evaluation frameworks, including task-specific metrics (e.g., mAP/IoU, geospatial accuracy), graph metrics, and GenAI quality measures.
 
 
 </li>
 </ul>
 
 Production ML &amp; MLOps
 
 
 
 <ul>
 <li>
 Build and launch models in production using best practices in CI/CD, model versioning, experiment tracking, and automated testing.
 </li>
 <li>
 Define and manage SLAs/SLOs for model services (latency, throughput, accuracy drift, availability).
 </li>
 <li>
 Implement monitoring for data quality, model drift, and operational health; drive incident triage and resolution.
 
 
 </li>
 </ul>
 
 Data &amp; platform collaboration
 
 
 
 <ul>
 <li>
 Collaborate with engineers, product managers, and analysts to understand business needs and data requirements.
 </li>
 <li>
 Partner with data infrastructure teams to improve dataset reliability, lineage, governance, and access patterns.
 </li>
 <li>
 Build and maintain production-grade data pipelines (extraction, transformation, loading) that support training and inference.
 
 
 </li>
 </ul>
 
 Operational excellence &amp; communication
 
 
 
 <ul>
 <li>
 Develop clear documentation for models, pipelines, evaluation artifacts, and operational runbooks.
 </li>
 <li>
 Communicate trade-offs and results to technical and non-technical stakeholders.
 </li>
 <li>
 Enjoy collaborating in a multicultural and distributed environment.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 Education
 
 
 
 <ul>
 <li>
 Master&rsquo;s degree in Computer Science, Statistics, Information Systems, or another quantitative field (PhD is a plus).
 
 
 </li>
 </ul>
 
 Experience
 
 
 
 <ul>
 <li>
 5+ years of experience in software engineering, data engineering, or ML engineering (or equivalent).
 </li>
 <li>
 3+ years of hands-on experience developing ML/AI algorithms and deploying them into production environments.
 </li>
 <li>
 Experience delivering GEOINT-grade analytics or products with quality controls (QA, traceability, auditability) and operational reliability expectations.
 
 
 </li>
 </ul>
 
 Technical competencies
 
 
 
 <ul>
 <li>
 Deep Learning frameworks (e.g., PyTorch, TensorFlow) and classical ML (e.g., scikit-learn, XGBoost).
 </li>
 <li>
 Geospatial intelligence &amp; GeoAI: raster/vector data fundamentals, map projections/CRS, tiling/geo-referencing, and quality considerations for intelligence products.
 </li>
 <li>
 Geospatial computer vision: detection/segmentation/classification, change detection, multi-spectral/SAR basics, and post-processing for geospatial products.
 </li>
 <li>
 Familiarity with geospatial tooling and data stacks (e.g., GDAL/rasterio, GeoPandas equivalents, PostGIS, cloud-optimized formats such as COG, STAC concepts, and OGC-style interoperability is a plus).
 </li>
 <li>
 Graph AI and topology-aware learning: experience with Graph Neural Networks (GNNs), graph embeddings, network representation learning, and modeling relational/topological structures.
 </li>
 <li>
 Familiarity with graph ML frameworks such as PyTorch Geometric, DGL, or similar libraries.
 </li>
 <li>
 Familiarity with modern AI patterns for augmenting workflows (e.g., embeddings/vector search, basic multimodal model integration) and how to evaluate them safely in production.
 </li>
 <li>
 ML system design: online/offline inference, batch scoring, model serving patterns, feature stores.
 </li>
 <li>
 Data engineering: big data pipelines, architectures and datasets, queuing and streaming processing.
 </li>
 <li>
 Strong programming skills in Python and at least one of Java / Scala / Go.
 </li>
 <li>
 Version control and collaboration tooling (GitHub, GitLab).
 
 
 </li>
 </ul>
 
 Ideally, you&rsquo;ll also need
 
 
 
 <ul>
 <li>
 Experience with geospatial / remote sensing / satellite imagery data.
 </li>
 <li>
 Highly scalable systems and data stores.
 </li>
 <li>
 Cloud platforms such as AWS or GCP (experience with managed ML services is a plus).
 </li>
 <li>
 Cartography experience (map design principles, symbology, labeling, scale/CRS considerations) and delivering analyst-friendly geospatial products.
 </li>
 <li>
 Photogrammetry / 3D reconstruction experience (e.g., SfM/MVS concepts, orthorectification, DEM/DSM generation, bundle adjustment fundamentals) and working with aerial/satellite imagery pipelines.
 </li>
 <li>
 Experience translating AI outputs into cartographic deliverables (e.g., map layers, annotations, styling rules, and uncertainty visualization).
 </li>
 <li>
 Familiarity with geospatial tooling and formats (e.g., raster/vector concepts, GDAL/rasterio equivalents), and deployment of geospatial analytics at scale.
 </li>
 <li>
 Experience with MLOps tooling (e.g., MLflow, Kubeflow, Airflow, DVC, Weights &amp; Biases).
 </li>
 <li>
 Containerization and orchestration (Docker, Kubernetes).
 </li>
 <li>
 LLM/VLM experience (nice-to-have): applying multimodal/LLM capabilities to GEOINT workflows (e.g., RAG over mission documentation, agentic tooling, structured extraction), plus practical evaluation and guardrails.
 </li>
 <li>
 Experience implementing GenAI safety patterns (PII handling, content filtering, jailbreak resistance), model governance, and security considerations.
 </li>
 <li>
 Experience building human-in-the-loop review workflows for GEOINT outputs (QA, adjudication, auditability) and operating under high-reliability expectations.
 
 
 </li>
 </ul>
 Job Details
 
 
 Role Level: Mid-Level Work Type: Full-Time Country: United Arab Emirates City: Abu Dhabi Company Website: https://space42.ai/ Job Function: Information Technology (IT) Company Industry/
 
 
 Sector: Space Research and Technology
 
 
 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.
 
 
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</div>

Senior AI Engineer Space42

Cyber Core for Information Technology

Job Description: AI Developer (React, Python &amp; Generative AI)Job Overview:<ul><li>We are seeking a highly analytical and technically proficient AI developer with extensive expertise in React.js, Python, and generative AI technologies.</li><li>The ideal candidate should demonstrate exceptional coding skills, strong logical reasoning, and the ability to design scalable, production-ready AI systems.</li><li>This role demands hands-on development experience, robust analytical thinking, and the ability to translate complex business requirements into efficient AI-driven solutions.</li></ul>Responsibilities &amp; Core Technical Abilities:<ul><li>Design, develop, and maintain end-to-end AI systems, encompassing model training, evaluation, deployment, monitoring, and continuous improvement.</li><li>Build production-grade applications using modern programming languages and tools, including TypeScript, JavaScript, Python, Node.js, Vite, React, and contemporary front-end build systems.</li><li>Develop scalable backend APIs and microservices capable of handling high-volume AI inference workloads using python and any other high level programming languages .</li><li>Architect and maintain Kubernetes clusters, ensuring resilient deployments, autoscaling, health checks, secrets management, and resource optimization.</li><li>Create and manage Helm charts for packaging, versioning, deploying, and continuously updating complex AI services across multiple environments.</li><li>Implement full MLOps pipelines, including data ingestion, feature engineering, model training, model validation, CI/CD workflows, and automated model deployment.</li><li>Integrate vector databases, embeddings pipelines, RAG architectures, and other retrieval-augmented systems to enhance AI reasoning capabilities.</li><li>Manage storage systems (object storage, block storage, distributed file systems) optimized for AI workloads, large model artifacts, and high-speed data access.</li><li>Build robust AI plugins, extensions, and integrations for third-party platforms with advanced functionality, including context handling, multi-agent coordination, and tool-use orchestration.</li><li>Optimize LLM performance using techniques like quantization, fine-tuning, LORA adapters, distillation, caching, and model parallelism.</li><li>Ensure system security, data privacy, identity management, and safe model behavior across all AI components.</li><li>Collaborate cross-functionally with product, data, and engineering teams to translate business requirements into scalable AI solutions.</li><li>Monitor production AI systems, implement logging/observability stacks, and troubleshoot reliability or performance issues.</li><li>Continuously research and adopt emerging AI/ML trends, frameworks, and MLOps best practices to maintain systems at industry-leading standards.</li></ul>Qualification and SkillsCore Technical Skills<ul><li>Strong proficiency in Python with a deep understanding of data structures and algorithms.</li><li>Hands-on experience in React.js, including hooks, state management, component architecture, and advanced knowledge of generative AI and LLM concepts.</li><li>Experience with OpenAI APIs or open-source LLMs, Langchain/llamaindex/agentic workflows, prompt engineering, Rag architecture, vector databases (FAISS, Pinecone, etc.), and strong understanding of REST API design and backend architecture.</li><li>Experience with relational or NoSQL databases and version control using Git.</li><li>Analytical &amp; Coding Strength (Mandatory)</li><li>Strong analytical thinking and structured problem-solving ability.</li><li>Ability to write optimized, scalable, and efficient code.</li><li>Strong debugging and root cause analysis skills.</li><li>Ability to analyze large code bases and improve performance.</li><li>Experience implementing algorithmic logic and performance tuning.</li><li>Practice of clean code and adherence to coding standards.</li></ul>Experience<ul><li>3+ years of hands-on experience delivering AI-driven applications.</li><li>Proven track record of writing production-grade code.</li></ul>Soft Skills<ul><li>Strong logical reasoning and critical thinking.</li><li>Excellent communication skills.</li><li>Ability to work independently and take ownership.</li><li>High attention to details and quality.</li></ul>Nice to Have<ul><li>Experience building AI workflows and autonomous bots.</li><li>Knowledge of model fine-tuning basics.</li><li>Experience with Docker and Kubernetes.</li><li>Understanding of AI evaluation metrics and benchmarking.</li></ul>Job Type: Full-timeWork Location: In person

AI Developer- (Resident Engineer – Onsite Client)

Happiest Minds Technologies

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 Job Description
 
 
 
 Design and implement Agentic AI architectures for banking-grade enterprise applications ? Build scalable Generative AI / RAG pipelines using Azure OpenAI and Azure AI Search ? Deliver production-grade AI systems with high availability, security, and performance Implement multimodal and multilingual AI solutions (Text + Vision, including English / Arabic) Develop document processing pipelines using Azure AI Document Intelligence Build Computer Vision workflows using OpenCV Develop REST APIs using FastAPI and deploy using Docker and Azure Containers Design embedding, vector search, and hybrid retrieval strategies Integrate Azure Cosmos DB / Document DB Implement CI/CD pipelines and Git-based workflows Collaborate closely with business stakeholders and engineering teams to deliver end-to-end AI solutions
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AI ENGINEERING SPECIALIST - AI ENGINEERING SPECIALIST

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 The laboratory of Dr. Djellel Difallah in the Division of Science, New York University Abu Dhabi, seeks a Post-Doctoral Associate or a Associate Research Scientist to join a lab focused on applied machine learning.
 
 
 The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience.
 
 
 
 Skills
 
 
 
 The ideal candidate is self-motivated and can work independently, has a passion for AI and its applications, and is willing to learn new technologies. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills include:
 
 
 <ul>
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 Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems
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 Graph Theory/Network Science
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 Python, and up-to-date machine learning libraries
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 Excellent written and verbal communication skills
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 Track record of publishing in top tier conferences
 
 
 </li>
 </ul>
 For consideration, applicants need to submit a cover letter, curriculum vitae, list of publications (if applicable), a 1-page statement of research interests, and three letters of reference. If you have any questions, please email: djellel@nyu.edu
 
 
 
 About NYUAD
 
 
 
 NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU&rsquo;s global network university, an interconnected network of portal campuses and academic centers across six continents that enable seamless international mobility of students and faculty in their pursuit of academic and scholarly activity. This global university represents a transformative shift in higher education, one in which the intellectual and creative endeavors of academia are shaped and examined through an international and multicultural perspective. As a major intellectual hub at the crossroads of the Arab world, NYUAD serves as a center for scholarly thought, advanced research, knowledge creation, and sharing, through its academic, research, and creative activities.
 
 
 EOE/AA/Minorities/Females/Vet/Disabled/Sexual Orientation/Gender Identity Employer
 
 
 UAE Nationals are encouraged to apply.
 
 
 
 Equal Employment Opportunity Statement
 
 
 
 
 For people in the EU, click here for information on your privacy rights under GDPR: www.nyu.edu/it/gdpr
 
 
 
 NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. All interested persons are encouraged to apply for vacant positions at all levels.
 
 
 
 Sustainability Statement
 
 
 
 NYU aims to be among the greenest urban campuses in the country and carbon neutral by 2040. Learn more at nyu.edu/sustainability
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Associate Research Scientist/ Post-Doctoral Associate in the Division of Science (Computer Science) - Dr. Djellel Difallah

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