civil engineer

mechanical engineer

electrical engineer

sales engineer

planning engineer

ai engineer

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
 
 
 
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</div>

Senior AI Engineer Space42

RE/MAX Smart Concept

Job Title: AI Automation EngineerLocation: Abu Dhabi, UAE Job Type: Full-Time | Office-Based Company: RE/MAX Smart Concept Real EstateAbout the PositionRE/MAX Smart Concept Real Estate is seeking a highly capable AI Automation Engineer to build and maintain automation workflows across our CRM and internal operational systems.This role is hands-on and execution focused, responsible for developing reliable automation systems, implementing API integrations, and ensuring long-term system stability.We are looking for a professional who can design and manage automation infrastructure that operates consistently, efficiently, and without disruption.Real Estate Industry Experience &ndash; Highly PreferredCandidates with experience working within real estate technology environments will be given priority, especially those familiar with:<ul><li>Real estate brokerage systems</li><li>Property management platforms</li><li>CRM environments used in real estate companies</li><li>Lead routing and distribution systems</li><li>Property listing integrations</li><li>Portal connectivity and data synchronization</li></ul>Understanding agent workflows, listing distribution, and real estate lead management will be considered a strong advantage.Key Responsibilities<ul><li>Design and implement automation workflows using Make, Zapier, or n8n</li><li>Integrate CRM systems using API-based read and write operations</li><li>Connect automation workflows with platforms such as:</li><li>Slack</li><li>Email systems</li><li>WhatsApp Business API</li><li>Implement structured logging, monitoring, retry mechanisms, and error handling</li><li>Test, document, and maintain automation systems</li><li>Ensure reliability, performance, and long-term operational stability</li><li>Maintain ownership of automation infrastructure and integrations</li></ul>Required Qualifications<ul><li>Strong hands-on experience with Make, Zapier, or n8n</li><li>Proven experience integrating CRM platforms through APIs (Bitrix24 or similar systems preferred)</li><li>Experience integrating Slack, Email services, and WhatsApp Business API</li><li>Solid understanding of:</li><li>REST APIs</li><li>Webhooks</li><li>Automation logic (Trigger &rarr; Action)</li><li>Data flow architecture</li><li>Error handling and retry strategies</li></ul>Important Notes<ul><li>This role focuses strictly on automation engineering and system execution</li><li>The position does not involve AI model training or LLM research</li><li>No responsibilities related to CRM redesign or consulting</li><li>Long-term ownership of automation systems is expected</li><li>We are looking for candidates with production-level experience and a strong focus on system reliability</li></ul>Employment DetailsEmployment Type: Full-Time Location: Abu Dhabi, UAE (Office-Based) Department: Technology / SystemsThis position offers long-term responsibility for automation infrastructure within a growing real estate organization.Application ProcessInterested candidates should submit their CV along with a short summary including:<ul><li>Examples of automation systems built in production</li><li>CRM platforms integrated</li><li>Tools and technologies used</li><li>Experience working with real estate companies (if applicable)</li></ul>Applications may be submitted via Indeed or sent directly to:hr@remax-sc.comJob Type: Full-timeExperience:<ul><li>Real Estate : 1 year (Required)</li></ul>Location:<ul><li>Abu Dhabi (Abu Dhabi) (Required)</li></ul>Work Location: In person

AI Automation Engineer - Real Estate

Happiest Minds Technologies

<div>
 
 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
</div>

AI ENGINEERING SPECIALIST - AI ENGINEERING SPECIALIST

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)

EPAM Systems

<div>
 
 EPAM is seeking a highly skilled and experienced
 
 AI Solutions Architect
 
 to join our team. The ideal candidate will be responsible for designing and implementing innovative solutions using
 
 Azure AI
 
 and
 
 Azure OpenAI
 
 services. This role is critical in automating and enhancing system maintenance notifications through AI-driven text generation, classification and validation, contributing to scalable and efficient system performance. You will work across the entire lifecycle of the product, from concept and design to deployment, ensuring seamless integration and continuous improvement.
 
 
 &nbsp;
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Design robust, scalable and secure cloud-native solutions that leverage Azure AI and OpenAI technologies and services
 </li>
 <li>
 Oversee the implementation process, ensuring alignment with business objectives and industry best practices
 </li>
 <li>
 Develop automated processes for classification, validation and generating text suggestions using Azure AI
 </li>
 <li>
 Integrate AI solutions into existing systems to optimize communication and data processing
 </li>
 <li>
 Establish and maintain a CI/CD pipeline for continuous integration and delivery using Azure DevOps
 </li>
 <li>
 Monitor cloud implementations and make recommendations for improvements in terms of performance, cost-efficiency and security
 </li>
 <li>
 Ensure that all cloud solutions comply with industry regulations and security standards
 </li>
 <li>
 Work closely with clients to understand their business needs and technical requirements
 </li>
 <li>
 Provide expert guidance and architectural recommendations for cloud modernization, configurations and optimizations
 </li>
 <li>
 Lead proof of concept (PoC) initiatives to validate proposed solutions and demonstrate their feasibility
 </li>
 <li>
 Foster a collaborative environment and ensuring successful project delivery
 </li>
 <li>
 Stay abreast of the latest Azure services and technologies
 </li>
 <li>
 Advocate for innovative Gen-AI cloud solutions that can enhance business performance and provide competitive advantages
 </li>
 </ul>
 
 
 
 
 Requirements
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Information Systems or a related field
 </li>
 <li>
 At least 5-7 years of experience working with Microsoft Azure, covering solution design, implementation and management in a cloud environment
 </li>
 <li>
 At least 2-3 years of experience with AI/Gen-AI technologies, specifically with Azure AI and Azure OpenAI services
 </li>
 <li>
 Extensive experience in architecting, deploying and managing solutions using Azure AI and Azure OpenAI services
 </li>
 <li>
 Deep understanding of Azure architecture, services and best practices
 </li>
 <li>
 Strong knowledge of natural language processing (NLP) and automated text generation
 </li>
 <li>
 Expertise in designing serverless architectures and integrating AI-driven automation solutions
 </li>
 <li>
 Hands-on experience with CI/CD pipelines and Azure DevOps
 </li>
 <li>
 Deep understanding of software development lifecycles and best practices in cloud environments
 </li>
 <li>
 Azure Solutions Architect Expert certification or other relevant Azure certifications
 </li>
 <li>
 Excellent verbal and written communication abilities; proficient at engaging with clients and stakeholders at all levels
 </li>
 <li>
 Strong analytical and problem-solving skills, with the ability to lead projects and collaborate with multiple stakeholders
 </li>
 </ul>
 
 
 
 
 Nice to have
 
 
 <ul>
 <li>
 Fluency in Arabic is strongly preferred for this role, as it will facilitate effective communication and collaboration with our regional teams and stakeholders
 </li>
 </ul>
 
 &nbsp;
 
 
 
 We offer
 
 
 <ul>
 <li>
 End of service gratuity
 </li>
 <li>
 Private healthcare and life insurance
 </li>
 <li>
 Employee assistance program
 </li>
 <li>
 Wellness program
 </li>
 <li>
 Annual air travel allowance for expatriates
 </li>
 <li>
 Regular performance feedback and salary reviews
 </li>
 <li>
 Global travel medical and accident insurance
 </li>
 <li>
 Referral bonuses
 </li>
 <li>
 Learning and development opportunities including in-house training and coaching, professional certifications, over 22,000 courses on LinkedIn Learning Solutions and much more
 </li>
 </ul>
</div>

Azure AI Solution Architect

Nagarro

<div>
 
 Job Description
 
 
 
 Design and deliver
 
 enterprise-grade AI solutions
 
 across LLM, ML, and knowledge engineering domains. Work with engineering teams to build
 
 scalable, secure, and cloud-native AI architectures
 
 aligned with enterprise standards.
 
 
 
 Key Responsibilities:
 
 
 
 <ul>
 <li>
 Architect solutions for LLM agents, RAG pipelines, Conversational AI, AI search, and knowledge systems.
 </li>
 <li>
 Design cloud-native AI platforms on Azure with serverless endpoints, GPU autoscaling, and distributed compute.
 </li>
 <li>
 Implement vector databases with advanced indexing and low-latency retrieval.
 </li>
 <li>
 Translate business vision into secure, compliant AI architectures with monitoring, RBAC, and explainability.
 </li>
 <li>
 Collaborate with stakeholders and communicate technical decisions clearly.
 
 
 </li>
 </ul>
 
 Must-Have Skills:
 
 
 
 <ul>
 <li>
 AI Application Solution Design
 </li>
 <li>
 Cloud-Native GenAI Development
 </li>
 <li>
 Data Science on Azure
 </li>
 <li>
 Machine Learning Solution Design
 </li>
 <li>
 Advanced RAG Workflow
 </li>
 <li>
 Hands-on Python and experience with agentic GenAI systems
 
 
 </li>
 </ul>
 Arabic proficiency is a plus.
</div>

AI Solution Architect

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