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Glow Beauty On Demand

Job Title ML Platform Engineer Generative AI &amp; Machine Learning Infrastructure Job Description ML Platform Engineer Generative AI &amp; Machine Learning Infrastructure Location: Dubai, UAE Industry: Technology / AI / Food &amp; Q-commerce Function: Machine Learning Engineering Gender: Any Candidate Nationality: Any Job Type: Full-time Role Overview Talabat, the leading on-demand food and Q-commerce app in the region, is seeking an experienced ML Platform Engineer to design, develop, and scale robust machine learning and generative AI platforms. The role involves building end-to-end ML infrastructure, enabling fast experimentation, efficient model deployment, and seamless monitoring to support next-generation AI-driven solutions for millions of users. Key Responsibilities * Build scalable ML platforms supporting data ingestion, model training, deployment, and monitoring for both traditional ML and generative AI models * Design standardized ML workflows using tools like MLflow, Kubeflow, and implement CI/CD pipelines with Docker and Kubernetes * Optimize generative AI deployments involving transformers, embeddings, RAG systems, and vector databases such as Pinecone, Redis, or Weaviate * Implement real-time serving frameworks like TensorFlow Serving, NVIDIA Triton, or Seldon for production-grade inference * Automate model lifecycle management and implement observability measures to track performance, detect drift, and maintain reliability * Collaborate with data engineering and product teams to align ML infrastructure with strategic business goals * Drive cloud infrastructure integration using AWS or GCP services, including Kubernetes clusters, managed ML services, and serverless components * Ensure cost efficiency, performance optimization, and adherence to best practices for MLOps and generative AI workflows Qualifications and Experience * Bachelor s degree in Computer Science, Engineering, or related field (Master s preferred) * Minimum 3 years of experience in ML platform engineering, MLOps, or generative AI infrastructure roles * Expertise in Python, ML frameworks (TensorFlow, PyTorch), and APIs such as Hugging Face and LangChain * Hands-on experience with containerization, orchestration (Docker, Kubernetes), and infrastructure-as-code tools (Terraform, Helm) * Proven ability to build real-time inference pipelines integrated with feature stores and streaming platforms like Kafka or Kinesis * Familiarity with SQL for data processing and knowledge of observability tools for monitoring AI systems * Strong understanding of model optimization techniques including quantization, batching, and prompt-tuning strategies

ML Platform Engineer

Job Title ML Platform Engineer Generative AI &amp; Machine Learning Infrastructure Job Description ML Platform Engineer Generative AI &amp; Machine Learning Infrastructure Location: Dubai, UAE Industry: Technology / AI / Food &amp; Q-commerce Function: Machine Learning Engineering Gender: Any Candidate Nationality: Any Job Type: Full-time Role Overview Talabat, the leading on-demand food and Q-commerce app in the region, is seeking an experienced ML Platform Engineer to design, develop, and scale robust machine learning and generative AI platforms. The role involves building end-to-end ML infrastructure, enabling fast experimentation, efficient model deployment, and seamless monitoring to support next-generation AI-driven solutions for millions of users. Key Responsibilities * Build scalable ML platforms supporting data ingestion, model training, deployment, and monitoring for both traditional ML and generative AI models * Design standardized ML workflows using tools like MLflow, Kubeflow, and implement CI/CD pipelines with Docker and Kubernetes * Optimize generative AI deployments involving transformers, embeddings, RAG systems, and vector databases such as Pinecone, Redis, or Weaviate * Implement real-time serving frameworks like TensorFlow Serving, NVIDIA Triton, or Seldon for production-grade inference * Automate model lifecycle management and implement observability measures to track performance, detect drift, and maintain reliability * Collaborate with data engineering and product teams to align ML infrastructure with strategic business goals * Drive cloud infrastructure integration using AWS or GCP services, including Kubernetes clusters, managed ML services, and serverless components * Ensure cost efficiency, performance optimization, and adherence to best practices for MLOps and generative AI workflows Qualifications and Experience * Bachelor s degree in Computer Science, Engineering, or related field (Master s preferred) * Minimum 3 years of experience in ML platform engineering, MLOps, or generative AI infrastructure roles * Expertise in Python, ML frameworks (TensorFlow, PyTorch), and APIs such as Hugging Face and LangChain * Hands-on experience with containerization, orchestration (Docker, Kubernetes), and infrastructure-as-code tools (Terraform, Helm) * Proven ability to build real-time inference pipelines integrated with feature stores and streaming platforms like Kafka or Kinesis * Familiarity with SQL for data processing and knowledge of observability tools for monitoring AI systems * Strong understanding of model optimization techniques including quantization, batching, and prompt-tuning strategies

ML Platform Engineer Generative AI & Machine Learning Infrastructure

Job Title ML Platform Engineer Generative AI &amp; Machine Learning Infrastructure Job Description ML Platform Engineer Generative AI &amp; Machine Learning Infrastructure Location: Dubai, UAE Industry: Technology / AI / Food &amp; Q-commerce Function: Machine Learning Engineering Gender: Any Candidate Nationality: Any Job Type: Full-time Role Overview Talabat, the leading on-demand food and Q-commerce app in the region, is seeking an experienced ML Platform Engineer to design, develop, and scale robust machine learning and generative AI platforms. The role involves building end-to-end ML infrastructure, enabling fast experimentation, efficient model deployment, and seamless monitoring to support next-generation AI-driven solutions for millions of users. Key Responsibilities * Build scalable ML platforms supporting data ingestion, model training, deployment, and monitoring for both traditional ML and generative AI models * Design standardized ML workflows using tools like MLflow, Kubeflow, and implement CI/CD pipelines with Docker and Kubernetes * Optimize generative AI deployments involving transformers, embeddings, RAG systems, and vector databases such as Pinecone, Redis, or Weaviate * Implement real-time serving frameworks like TensorFlow Serving, NVIDIA Triton, or Seldon for production-grade inference * Automate model lifecycle management and implement observability measures to track performance, detect drift, and maintain reliability * Collaborate with data engineering and product teams to align ML infrastructure with strategic business goals * Drive cloud infrastructure integration using AWS or GCP services, including Kubernetes clusters, managed ML services, and serverless components * Ensure cost efficiency, performance optimization, and adherence to best practices for MLOps and generative AI workflows Qualifications and Experience * Bachelor s degree in Computer Science, Engineering, or related field (Master s preferred) * Minimum 3 years of experience in ML platform engineering, MLOps, or generative AI infrastructure roles * Expertise in Python, ML frameworks (TensorFlow, PyTorch), and APIs such as Hugging Face and LangChain * Hands-on experience with containerization, orchestration (Docker, Kubernetes), and infrastructure-as-code tools (Terraform, Helm) * Proven ability to build real-time inference pipelines integrated with feature stores and streaming platforms like Kafka or Kinesis * Familiarity with SQL for data processing and knowledge of observability tools for monitoring AI systems * Strong understanding of model optimization techniques including quantization, batching, and prompt-tuning strategies What We Offer * Opportunity to work on advanced ML and AI projects impacting millions of users in the Middle East * Competitive salary and benefits package * A collaborative, innovation-driven environment with cutting-edge tools and technologies * Career development in one of the fastest-growing tech ecosystems in the region

ML Platform Engineer Generative AI & Machine Learning

Delivery Hero

<div>Company Description 
Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat&rsquo;s local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region.
We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily.
Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner. 
Job Description 
Summary
 As the leading delivery platform in the region, we have a unique responsibility and opportunity to positively impact millions of customers, restaurant partners, and riders. To achieve our mission, we must scale and continuously evolve our machine learning capabilities, including cutting-edge Generative AI (genAI) initiatives. This demands robust, efficient, and scalable ML platforms that empower our teams to rapidly develop, deploy, and operate intelligent systems.
As an ML Platform Engineer, your mission is to design, build, and enhance the infrastructure and tooling that accelerates the development, deployment, and monitoring of traditional ML and genAI models at scale. You&rsquo;ll collaborate closely with data scientists, ML engineers, genAI specialists, and product teams to deliver seamless ML workflows&mdash;from experimentation to production serving&mdash;ensuring operational excellence across our ML and genAI systems. 
Qualifications 
Responsibilities<ul><li>
Design, build, and maintain scalable, reusable, and reliable ML platforms and tooling that support the entire ML lifecycle, including data ingestion, model training, evaluation, deployment, and monitoring for both traditional and generative AI models. 
</li><li>Develop standardized ML workflows and templates using MLflow and other platforms, enabling rapid experimentation and deployment cycles. 
</li><li>Implement robust CI/CD pipelines, Docker containerization, model registries, and experiment tracking to support reproducibility, scalability, and governance in ML and genAI. 
</li><li>Collaborate closely with genAI experts to integrate and optimize genAI technologies, including transformers, embeddings, vector databases (e.g., Pinecone, Redis, Weaviate), and real-time retrieval-augmented generation (RAG) systems. 
</li><li>Automate and streamline ML and genAI model training, inference, deployment, and versioning workflows, ensuring consistency, reliability, and adherence to industry best practices. 
</li><li>Ensure reliability, observability, and scalability of production ML and genAI workloads by implementing comprehensive monitoring, alerting, and continuous performance evaluation. 
</li><li>Integrate infrastructure components such as real-time model serving frameworks (e.g., TensorFlow Serving, NVIDIA Triton, Seldon), Kubernetes orchestration, and cloud solutions (AWS/GCP) for robust production environments. 
</li><li>Drive infrastructure optimization for generative AI use-cases, including efficient inference techniques (batching, caching, quantization), fine-tuning, prompt management, and model updates at scale. 
</li><li>Partner with data engineering, product, infrastructure, and genAI teams to align ML platform initiatives with broader company goals, infrastructure strategy, and innovation roadmap. 
</li><li>Contribute actively to internal documentation, onboarding, and training programs, promoting platform adoption and continuous improvement.</li></ul>
Requirements
Technical Experience<ul><li>
Strong software engineering background with experience in building distributed systems or platforms designed for machine learning and AI workloads. 
</li><li>Expert-level proficiency in Python and familiarity with ML frameworks (TensorFlow, PyTorch), infrastructure tooling (MLflow, Kubeflow, Ray), and popular APIs (Hugging Face, OpenAI, LangChain). 
</li><li>Experience implementing modern MLOps practices, including model lifecycle management, CI/CD, Docker, Kubernetes, model registries, and infrastructure-as-code tools (Terraform, Helm). 
</li><li>Demonstrated experience working with cloud infrastructure, ideally AWS or GCP, including Kubernetes clusters (GKE/EKS), serverless architectures, and managed ML services (e.g., Vertex AI, SageMaker). 
</li><li>Proven experience with generative AI technologies: transformers, embeddings, prompt engineering strategies, fine-tuning vs. prompt-tuning, vector databases, and retrieval-augmented generation (RAG) systems. 
</li><li>Experience designing and maintaining real-time inference pipelines, including integrations with feature stores, streaming data platforms (Kafka, Kinesis), and observability platforms. 
</li><li>Familiarity with SQL and data warehouse modeling; capable of managing complex data queries, joins, aggregations, and transformations. 
</li><li>Solid understanding of ML monitoring, including identifying model drift, decay, latency optimization, cost management, and scaling API-based genAI applications efficiently.</li></ul>
Qualifications<ul><li>
Bachelor&rsquo;s degree in Computer Science, Engineering, or a related field; advanced degree is a plus. 
</li><li>2+ years in a tech lead role, 5+ years of experience in ML platform engineering, ML infrastructure, generative AI, or closely related roles.</li><li>
Proven track record of successfully building and operating ML infrastructure at scale, ideally supporting generative AI use-cases and complex inference scenarios. 
</li><li>Strategic mindset with strong problem-solving skills and effective technical decision-making abilities. 
</li><li>Excellent communication and collaboration skills, comfortable working cross-functionally across diverse teams and stakeholders. 
</li><li>Strong sense of ownership, accountability, pragmatism, and proactive bias for action.</li></ul></div>

Staff Engineer - ML Platform

Superbytes

Job Description: AI DeveloperLocation: Dubai, UAEJob Type: Full-timeExperience Required: 1 – 4 years About the RoleWe are seeking a highly skilled and motivated AI Developer to join our team in Dubai. The ideal candidate will have hands-on experience in Generative AI, Large Language Models (LLMs), Low Parameter (LoRA/LLP) fine-tuning, and Mixture of Experts (MoE) frameworks, with the ability to translate these technologies into innovative product solutions.This role requires strong analytical skills, proficiency in advanced Excel and Power BI, and the ability to work on AI-driven product development in a high-paced, data-intensive environment. Key Responsibilities1. Design, develop, and deploy AI solutions using Generative AI, LLMs, LLP, and MoE architectures.2. Build, fine-tune, and optimize models for real-world product applications.3. Collaborate with product, data, and engineering teams to integrate AI into scalable platforms.4. Develop and maintain data pipelines for training and inference.5. Utilize advanced Excel and Power BI for data analysis, visualization, and reporting.6. Conduct performance evaluations, testing, and continuous improvement of AI models.7. Research and stay updated on the latest advancements in AI/ML and apply them to product innovation.

AI Developer

Google

Minimum qualifications:Bachelor's degree in Computer Science, Information Technology, Engineering, a related technical field, or equivalent practical experience.5 years of experience in software development.Experience programming with Python.Experience with data science or machine learning.Preferred qualifications:5 years of experience with data structures/algorithms.Experience architecting and developing software or infrastructure for scalable, distributed systems and with machine learning technologies. Experience with GenAI, AI Agents, or related technologies.Experience building scalable end to end software architectures that use agentic or GenAI driven capabilities.Understanding of Responsible AI practices.About The JobGoogle's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.The Google Cloud Platform Generative AI Blackbelt team helps customers unlock their potential with AI. As part of an entrepreneurial team in this rapidly growing business, you will help shape the future of businesses of all sizes using AI and ML.Applied AI builds conversational agents deployed at a large scale that achieve very meaningful results in the real world. Some examples include the customer agent built for large call center environments, to fast food ordering handled by our Food AI agent. The team is transforming how enterprises connect with customers through the power of AI. We also offer unique experiences for team members where you get to work directly with the model builders (Google DeepMind / Vertex), learn and work with brilliant AI leaders, and have access to Global 1000 customers via our existing Google Cloud relationships. The opportunity in this space is tremendous.ResponsibilitiesWork with the team to identify and qualify business opportunities, understand key customer technical objections, and develop the strategy to resolve technical blockers.Provide AI expertise to support the technical relationship with Google s customers, manage product and solution briefings, create demos, proof-of-concept work, and partner directly with product management to prioritize solutions impacting customer adoption to Google Cloud.Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to implement a complete solution Google Cloud.Support developers, creators, and enterprises to leverage Google s Generative Language APIs so they can build their own AI products in the future.Travel to customer sites, conferences, and other related events as needed.Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .

Senior Software Engineer

Cerebras

<div>
 
 
 Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.&nbsp;
 
 
 &nbsp;
 
 
 
 
 Cerebras' current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.
 
 
 
 
 About the Role:
 
 
 
 Would you like to participate in creating the fastest Generative Models inference in the world? Join the Cerebras Inference Team to participate in development of unique Software and Hardware combination that sports best inference characteristics in the market while running largest models available.&nbsp;
 
 
 Cerebras wafer scale inference platform allows running Generative models with unprecedented speed thanks to unique hardware architecture that provides fastest access to local memory, ultra-fast interconnect and huge amount of available compute.&nbsp;
 
 
 You will be part of the team that works with latest open and closed generative AI models to optimize for the Cerebras inference platform. Your responsibilities will include working on model representation, optimization and compilation stack to produce the best results on Cerebras current and future platforms.&nbsp;&nbsp;
 
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Analysis of new models from generative AI field and understanding of impacts on compilation stack
 </li>
 <li>
 Develop and maintain model definition framework that consists of model building blocks to represent large language models based on PyTorch and Cerebras dialects ready to be deployed on Cerebras hardware.
 </li>
 <li>
 Develop and maintain the&nbsp;
 
 frontend compiler infrastructure
 
 &nbsp;that ingests PyTorch models and produces an intermediate representation (IR).
 </li>
 <li>
 Extend and optimize&nbsp;
 
 PyTorch FX / TorchScript / TorchDynamo
 
 -based tooling for graph capture, transformation, and analysis.
 </li>
 <li>
 Collaboration with other teams throughout feature implementation
 </li>
 <li>
 Research on new methods for model optimization to improve Cerebras inference
 </li>
 </ul>
 
 &nbsp;
 
 Qualifications:
 
 
 <ul>
 <li>
 Degree in Engineering, Computer Science, or equivalent in experience and evidence of exceptional ability
 </li>
 <li>
 Strong&nbsp;
 
 Python
 
 &nbsp;programming skills and in-depth experience with&nbsp;
 
 PyTorch internals
 
 &nbsp;(e.g., TorchScript, FX, or Dynamo).
 </li>
 <li>
 Solid understanding of&nbsp;
 
 computational graphs, tensor operations, and model tracing
 
 .
 </li>
 <li>
 Experience building or extending&nbsp;
 
 compilers, interpreters, or ML graph optimization frameworks
 
 .
 </li>
 <li>
 Experience working with PyTorch and HuggingFace Transformers library
 </li>
 <li>
 Knowledge and experience working with Large Language Models (understanding Transformer architecture variations, generation cycle, etc.)
 </li>
 <li>
 Strong&nbsp;
 
 C++
 
 &nbsp;programming skills.
 </li>
 <li>
 Knowledge of&nbsp;
 
 MLIR
 
 &nbsp;based compilation stack
 </li>
 </ul>
 
 
 Preferred Qualifications
 
 
 <ul>
 <li>
 Prior experience contributing to&nbsp;
 
 PyTorch
 
 ,&nbsp;
 
 TensorFlow XLA
 
 ,&nbsp;
 
 TVM
 
 ,&nbsp;
 
 ONNX RT
 
 , or similar compiler stacks.
 </li>
 <li>
 Knowledge of&nbsp;
 
 hardware accelerators
 
 ,&nbsp;
 
 quantization
 
 , or&nbsp;
 
 runtime scheduling
 
 .
 </li>
 <li>
 Experience with&nbsp;
 
 multi-target inference compilation
 
 &nbsp;(e.g., CPU, GPU, custom ASICs).
 </li>
 <li>
 Understanding of&nbsp;
 
 numerical precision trade-offs
 
 &nbsp;and&nbsp;
 
 operator lowering
 
 .
 </li>
 <li>
 Contributions to open-source ML compiler projects.
 </li>
 </ul>
 
 &nbsp;
 
 
 &nbsp;
 
 &nbsp;
 
 Why Join Cerebras
 
 
 People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we&rsquo;ve reached an inflection&nbsp; point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
 
 <ol>
 <li>
 Build a breakthrough AI platform beyond the constraints of the GPU.
 </li>
 <li>
 Publish and open source their cutting-edge AI research.
 </li>
 <li>
 Work on one of the fastest AI supercomputers in the world.
 </li>
 <li>
 Enjoy job stability with startup vitality.
 </li>
 <li>
 Our simple, non-corporate work culture that respects individual beliefs.
 </li>
 </ol>
 
 Read our blog:&nbsp;Five Reasons to Join Cerebras in 2025.
 
 Apply today and become part of the forefront of groundbreaking advancements in AI!
 
 
 Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer.&nbsp;
 
 
 We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies.
 
 
 We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
 
 
 
 
 This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.
 
 
</div>

Python / PyTorch Developer — Frontend Inference Compiler – Dubai

Explore 1950+ Generative ai engineer in Dubai United Arab Emirates this November. Find roles across top companies and apply now to boost your career.