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Glow Beauty On Demand

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

ML Platform Engineer

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

ML Platform Engineer Generative AI & Machine Learning Infrastructure

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

ML Platform Engineer Generative AI & Machine Learning

Core42

<p><strong>Responsibilities</strong></p>
<ul>
<li>Design &amp; Develop Data Pipelines: Build, optimize, and maintain ETL/ELT pipelines to ingest, transform, and process large volumes of structured and unstructured data from diverse sources. Leverage Azure Data Factory, Azure Data Lake, Azure Synapse Analytics, Microsoft Fabric, or Databricks for scalable data integration and transformation.</li>
<li>Data Modeling &amp; Architecture: Design and implement data models and schemas optimized for analytics, machine learning, and AI-driven decision-making. Create and manage data warehouses, data lakes, and lakehouses to support data analytics and AI workloads.</li>
<li>Data Governance &amp; Security: Ensure data quality, compliance, and security by implementing governance frameworks and leveraging tools like Microsoft Purview. Enforce data security protocols, including role-based access control, data masking, and encryption.</li>
<li>AI/ML Data Integration: Collaborate with data scientists to integrate data pipelines with machine learning workflows, enabling seamless training and deployment of models.</li>
<li>Performance Optimization: Monitor and optimize the performance of data pipelines and cloud resources to ensure high availability, scalability, and cost efficiency.</li>
</ul>
<p><strong>Secondary Responsibilities</strong></p>
<ul>
<li>Build AI Scoring Engines: Develop and implement AI scoring engines to automate decision-making processes, such as fraud detection, customer segmentation, and recommendation systems.</li>
<li>Data Preparation for AI/ML Models: Partner with data scientists to prepare and preprocess data for machine learning models, including handling missing values, scaling, and feature engineering.</li>
<li>AI/ML Model Development: Contribute to the development of predictive and prescriptive models using frameworks like scikit-learn, TensorFlow, PyTorch, and Azure ML Studio.</li>
<li>Model Deployment and Integration: Deploy machine learning models and scoring engines to production environments using Azure Machine Learning, integrating real-time and batch workflows.</li>
<li>MLOps Implementation: Build and maintain MLOps pipelines for versioning, monitoring, and retraining AI models in production, ensuring continuous improvement.</li>
<li>AI-Driven Insights: Support the integration of AI models with business applications to deliver actionable insights and improve operational efficiency.</li>
</ul>

Data Engineer

GSSTech Group

<div><p>We are seeking a highly skilled <b>Senior DevOps Engineer</b> with experience in architecting and deploying <b>Agentic AI systems</b>. The ideal candidate will have a strong background in DevOps, cloud infrastructure, and modern AI integrations, including LLM-based systems and autonomous agent development.</p><p><b>
Requirements</b></p><h3 class="jobSectionHeader"><b>
Key Responsibilities:</b></h3><ul>
<li><b>Architect AI-Driven Systems:</b> Design and implement modular architectures that integrate large language models (LLMs) and support multi-step reasoning, retrieval-augmented generation (RAG), and external tool usage.
</li><li><b>AI Model Integration:</b> Evaluate, select, and integrate suitable AI models (e.g., GPT-5, custom LLMs) into scalable production environments.
</li><li><b>Agent Development:</b> Build and deploy intelligent agents capable of research, summarization, task automation, and action execution.
</li><li><b>Infrastructure &amp; Scalability:</b> Manage containerized environments using Docker and Kubernetes, ensuring high availability, performance, and security.
</li><li><b>Responsible AI Practices:</b> Establish and maintain content filtering, audit logging, and data protection mechanisms to ensure ethical AI operations.
</li><li><b>Stakeholder Collaboration:</b> Work closely with cross-functional teams to translate business needs into scalable technical AI solutions.
</li><li><b>Continuous Optimization:</b> Monitor agent performance, analyze errors, refine prompts, and optimize system behavior for accuracy and efficiency.
</li></ul><h3 class="jobSectionHeader"><b>Requirements:</b></h3><ul>
<li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or a related field.
</li><li>5+ years of experience in DevOps or cloud infrastructure engineering.
</li><li>Strong hands-on experience with Docker, Kubernetes, and CI/CD pipelines.
</li><li>Experience with LLMs, AI model deployment, or MLOps workflows is a strong plus.
</li><li>Knowledge of cloud platforms (AWS, Azure, or GCP) and infrastructure as code (Terraform, Ansible, etc.).
</li><li>Excellent problem-solving skills and the ability to work in a fast-paced, AI-focused environment.
</li></ul><h3 class="jobSectionHeader"><b>Preferred Skills:</b></h3><ul>
<li>Experience with Python-based AI frameworks and APIs.
</li><li>Familiarity with retrieval systems, vector databases, or RAG pipelines.
</li><li>Understanding of data governance, compliance, and responsible AI principles.</li></ul></div>

Senior DevOps Engineer - AI & Agentic Systems

<div><div><b><i>About Emirates Global Aluminium</i></b><i>
</i></div><div>Emirates Global Aluminium is the world&rsquo;s biggest &lsquo;premium aluminium&rsquo; producer and the largest industrial company in the United Arab Emirates outside the oil and gas industry. EGA is an integrated aluminium producer, with operations on four continents from bauxite mining to the production of cast primary aluminium and recycling. EGA employs over 7,000 of these people including more than 1,200 UAE Nationals. EGA operates aluminium smelters in Jebel Ali and Al Taweelah in the United Arab Emirates, an alumina refinery in Al Taweelah, a bauxite mine and associated export facilities in the Republic of Guinea, a speciality foundry in high strength recycled aluminium in Germany, and a recycling plant in the United States.</div>
<div><b>
JOB PURPOSE:</b><br>
The Lead Data Ops Engineer will play a critical role in building, scaling, automating, and maintaining the company's Big Data infrastructure and Machine Learning operations (MLOps). This individual will work in close collaboration with our Data Architect, Data Science, Data Engineering, and IT teams to ensure the development, deployment and scale of robust, high-performance data processing systems and ML models.</div><br>
<div></div>
<div><b>KEY ACCOUNTABILITIES:</b></div>
<ul><li><div><b>Big Data Infrastructure:</b> Design, build, and maintain high-performance, cloud-based, fault-tolerant, scalable distributed Data infrastructure that supports the company&rsquo;s data-intensive applications. (Real time/Batch/LLM&rsquo;s). This includes developing strategies for data storage (TB&rsquo;s), processing, and analysis, and implementing high-performance, scalable data pipelines for ML models and data products, supporting up to 50-60 use cases a year and thousands of IoT devices.</div>
</li><li><div>Create infrastructure as Code, perform configuration and set up managed data services. Build and deploy a data science playground for research and prototyping for the professional and citizen data science program being rolled out and supporting 15-20 citizen data scientists/ambassadors.</div>
</li><li><div><b>Machine Learning Operations (MLOps):</b> Develop and manage the ML operational process, working closely with the data science team to implement ML models into production, including edge. This includes streamlining the ML lifecycle, from model development and testing to deployment and implementing the monitoring and alerting strategy.</div>
</li><li><div><b>Automation and Scalability:</b> Implement automation tools and frameworks to manage system updates/changes. Ensure that all systems and infrastructures can scale effectively with the increase in IoT sensors and devices.</div>
</li><li><div><b>Continuous Integration and Deployment (CI/CD):</b> Oversee continuous integration and continuous deployment practices for the data and ML pipeline, ensuring that software can be reliably released at any time.</div>
</li><li><div><b>System Monitoring and Reliability:</b> Monitor system performance and reliability to ensure high levels of performance, availability, and security. This includes identifying and fixing potential and existing system issues. Collaboration and Communication: Strong collaboration with Data Architect/Engineer, data scientists for the implementation and testing of new data services to provide an elastic data infrastructure.</div>
</li><li><div><b>Security:</b> Oversee and ensure that all Big Data and ML Ops platforms comply with the company's security standards and policies.</div>
</li><li><div><b>Mentorship and Leadership:</b> Act as a mentor to junior data members, providing guidance and support in their professional development. Promote a culture of performance, collaboration, and continuous learning within the data team.</div>
</li><li><div><b>Innovation and Continuous Improvement:</b> Stay up-to-date with industry trends and new technologies. Continuously explore innovative solutions and enhancements to the existing data architecture to improve its scalability, reliability, and efficiency.</div>
</li><li><div><b>Problem Solving:</b> Anticipate and resolve technical issues before they become roadblocks, maintaining the continuity of data flow and ensuring the highest levels of data quality and integrity.</div><br>
</li></ul><div></div><div><b>AUTHORITY/DECISION MAKING:</b></div>
<ul><li><div><b>Infrastructure Design:</b> Decide on the most effective design and implementation of the company's Big Data infrastructure.</div>
</li><li><div><b>ML Ops Process:</b> Make key decisions on the ML operational process, ensuring that ML models can be effectively integrated into production.</div>
</li><li><div><b>Automation Tools:</b> Choose the most appropriate automation tools and frameworks for the company's needs.</div>
</li><li><div><b>CI/CD Practices:</b> Determine the best practices for continuous integration and deployment in the context of the company's operations.</div>
</li><li><div><b>System Monitoring:</b> Make decisions on system monitoring strategies, including the selection of tools and responses to system performance metrics.</div>
</li><li><div><b>Security Policies:</b> Have a say in the implementation of security policies as they pertain to the Big Data and ML Ops platforms.</div>
</li><li><div><b>Budget and Costing:</b> Taking ownership of managing data platform costs and relevant data services.</div><br>
</li></ul><div></div><div><b>QUALIFICATIONS &amp; SKILLS:</b></div>
<div><b>Domain Expertise:</b></div>
<ul><li><div>Bachelor&rsquo;s degree required, MS or PhD preferred.</div>
</li><li><div>Bachelor&rsquo;s in Data Science, Computer Science, Engineering, Statistics and 10+ years of relevant experience.</div>
</li><li><div>Experience: A minimum of 5-7 years of experience in a DevOps role, with a focus on managing Big Data infrastructures and MLOps.</div>
</li></ul><div><b>Technical Skills:</b></div>
<ul><li><div>Strong experience with Big Data technologies such as Hadoop, Spark, Kafka, etc.</div>
</li><li><div>Proven expertise in managing and deploying ML models into production.</div>
</li><li><div>Proficient in using CI/CD tools like Jenkins, Travis CI, CircleCI, etc.</div>
</li><li><div>Proficient in using infrastructure automation tools like Terraform, Chef, Puppet, Ansible, etc.</div>
</li><li><div>Strong knowledge of cloud platforms such as Azure (AWS, GCP).</div>
</li><li><div>Experience with containerization technologies like Docker, Kubernetes, etc.</div>
</li><li><div>Familiarity with various database technologies, both SQL and NoSQL.</div>
</li><li><div>Proficiency in programming languages such as Python, Java, or Scala.</div>
</li><li><div>Experience of leveraging MS/Azure ecosystem to manage the development and maintenance of cloud platform operations.</div>
</li><li><div>A broad set of technical skills and knowledge across hardware, software, systems and solutions development.</div>
</li><li><div>A proven track record of using quantitative analysis to impact key business or product decisions.</div>
</li><li><div>Solid grasp of and experience with implementing and operating software development methodologies.</div>
</li><li><div>A solid grasp of common statistical applications and methods (A/B tests and multivariate experiments, probabilities, regression).</div>
</li><li><div>Understanding of Agile Software Development Lifecycle and project planning/execution skills.</div>
</li><li><div>Outstanding communication skills with stakeholders at all levels, managing stakeholders&rsquo; expectations and facilitating discussions across high-risk or complexity under constrained timescales.</div>
</li><li><div>Outstanding capability to establish enterprise-scale data integration procedures across the data development life cycle and ensure that teams adhere to these. Able to manage resources to ensure that data services work effectively at an enterprise level.</div>
</li><li><div>Up to date with data innovation and expert in investigating emerging trends in data-related approaches, performing horizon-scanning for the organization and introducing innovative ways of working.</div>
</li><li><div>Expert in Data integration design and can establish standards and well informed on best practices across different industries. The candidate can distinguish how to keep those standards up to date and ensures adherence to them.</div>
</li><li><div>Good understanding of concepts and principles of data modeling and can produce, maintain and update relevant data models for specific business needs. Also, shows good knowledge on how to reverse-engineer data models from a live system.</div>
</li><li><div>Expert in Metadata management and understands how metadata repositories can support different areas of the business. Capable of promoting and communicating the value of metadata repositories and knows how to set up robust governance processes to keep repositories up to date.</div>
</li><li><div>Expert in identifying and anticipating problems and knowing how to prevent them by linking how problems fit into the larger picture. Has the ability to identify and describe problems, help others to describe them, and build problem-solving capabilities in others.</div>
</li><li><div>Expert in setting up team-based data engineering standards for programming tools and techniques and can select appropriate development methods. Acts as an advisor on the application of standards and methods and ensures compliance while taking technical responsibility for all stages and/or iterations in a software development project, providing method-specific technical advice and guidance to project stakeholders.</div>
</li><li><div>Technical Expert in predicting and advising on data engineering future technology changes that present opportunities for a product or program.</div>
</li><li><div>Experienced with reviewing requirements, specifications and defining test conditions with good understanding on how to identify issues and risks associated with work while being able to analyze and report test activities and results.</div>
</li></ul><div><b>Agile/Digital Experience:</b></div>
<ul><li><div>Experience in Agile Development, with specific Data Engineer/Data Architect (or similar) experience preferred.</div>
</li><li><div>Understands relationships with Product Owner, Agile Coach, Data Scientist, Designer, and the rest of the technical team.</div>
</li></ul><div><b>Individual Skills:</b></div>
<ul><li><div>Exceptional problem-solving skills: demonstrated ability to understand business challenges, structure complex problems, develop solutions.</div>
</li><li><div>Ability to partner and influence key business stakeholders at all levels of the organization.</div>
</li><li><div>Strong skills in team leadership and networking, ability to work across multiple organizations to accomplish diverse goals.</div>
</li><li><div>Exceptional presentation, written, and verbal communication skills.</div>
</li><li><div>Strong communication skills with the ability to align the organization on complex technical decisions.</div>
</li><li><div>Active coach and mentor whose goal is to grow and maximize the team&rsquo;s potential.</div>
</li><li><div>Strong ability and enthusiasm around data strategy and ability to inspire team and organization around the usage of data.</div>
</li></ul><div><b>Mindset &amp; Behaviors:</b></div>
<ul><li><div>High energy and passionate individual who inspires teammates to reach their maximum potential.</div>
</li><li><div>Empathetic coach who can help develop a new group of highly motivated data engineers.</div>
</li><li><div>Excited about trying new solutions outside standard approval.</div>
</li><li><div>Invested in developing a culture of trust, free thought, and complete transparency.</div></li></ul></div>

Lead Data Ops Engineer

DataRobot

<div><div>Job Description:</div><div></div><div>
DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business &mdash; today and in the future.</div><div></div><div>
As an AI Engineer on our Professional Services team, you will be at the forefront of the AI revolution, working directly with our most strategic customers. You'll be a trusted advisor and hands-on builder, translating complex business challenges into cutting-edge AI solutions that deliver tangible business value.</div><div></div><div>
This is a unique opportunity to design, build, and deploy a wide range of applications&mdash;from powerful predictive models to sophisticated Generative AI agents and chatbots. If you thrive on solving real-world problems and want to work with the latest in AI technology, this role is for you.</div><div></div><div>
This role is based in Dubai, United Arab Emirates.</div><div></div><div>
This is a fully remote position with no requirement to go into an office on a regular basis. There will be travel requirements associated with this position to visit clients onsite up to 25% - 50% of the time.</div><div></div><div><br>
Key Responsibilities:</div><ul><li><div>
Partner with Customers: Collaborate closely with customer stakeholders to understand their business goals, identify high-impact use cases, and define technical requirements for AI solutions.</div></li><li><div>
Build &amp; Deploy AI Solutions: Design, develop, and deploy end-to-end AI solutions using the DataRobot platform and open-source tools. This includes:</div></li><li><div>
Agentic AI: Developing and deploying agents on DataRobot leveraging common frameworks such as Langgraph, CrewAI, Llama Index</div></li><li><div>
Generative AI: Building custom GenAI chatbots, Retrieval-Augmented Generation (RAG) systems.</div></li><li><div>
Predictive AI: Developing and deploying classic machine learning models for use cases like forecasting, churn prediction, and fraud detection.</div></li><li><div>
Serve as a Technical Expert: Act as a subject matter expert on the DataRobot platform and modern AI/ML development, guiding customers on best practices for MLOps, model governance, and scaling AI initiatives.</div></li><li><div>
Deliver Value: Ensure that the solutions you build are robust, scalable, and directly contribute to the customer's business objectives.</div></li><li><div>
Communicate &amp; Collaborate: Clearly communicate complex technical concepts and project outcomes to both technical and non-technical audiences, from data scientists to C-level executives.</div></li></ul><div></div><div><br>
Knowledge, Skills and Abilities:</div><div></div><div>
AI &amp; Machine Learning Expertise:</div><ul><li><div>
Strong proficiency in Python and common data science libraries (e.g., pandas, scikit-learn, NumPy, etc.).</div></li><li><div>
Practical experience with Generative AI technologies, including Large Language Models (LLMs), vector databases,</div></li><li><div>
Solid understanding of the end-to-end agentic AI lifecycle from building agents in frameworks like LangGraph or CrewAI, to at scale deployment and monitoring.</div></li></ul><div></div><div>
Application Development &amp; Operations:</div><ul><li><div>
Demonstrable experience developing and deploying applications, including building REST APIs (e.g., using Flask, FastAPI) to serve ML models and GenAI logic.</div></li><li><div>
Proficiency with containerization using Docker and experience deploying and managing applications on container orchestration platforms like Kubernetes (K8s).</div></li><li><div>
Solid understanding of secure application development practices, including authentication/authorization (e.g., OAuth, API keys), secrets management, and securing public-facing endpoints.</div></li><li><div>
Customer Focus: Experience in a client-facing or consulting role with exceptional verbal and written communication skills. You must be comfortable leading technical discussions and presenting to diverse audiences.</div></li><li><div>
Problem-Solving Mindset: A deep curiosity and a passion for solving complex, unstructured problems.</div></li></ul><div></div><div><br>
Requisite Education and Experience / Minimum Qualifications:</div><ul><li><div>
Experience: Approximately 6-8 years of hands-on experience in AI Application development, software engineering, machine learning engineering, or a similar role with a proven track record of deploying AI solutions or applications into production.</div></li><li><div>
Education: A Master&rsquo;s Degree or Ph.D. in Computer Science, Statistics, Artificial Intelligence, Engineering, or a related quantitative field.</div></li><li><div>
Cloud Experience: Hands-on experience with a major cloud platform (AWS, Azure, or GCP).</div></li><li><div>
DataRobot Experience: Familiarity with the DataRobot AI Platform is a strong plus.</div></li><li><div>
MLOps Knowledge: Understanding of MLOps principles and tools for model CI/CD, monitoring, and governance.</div></li><li><div>
Fluency in English and Arabic</div></li><li><div>
Willingness to travel onsite with clients up to 40% of the time</div></li></ul><div></div><div><br>
The talent and dedication of our employees are at the core of DataRobot&rsquo;s journey to be an iconic company. We strive to attract and retain the best talent by providing competitive pay and benefits with our employees&rsquo; well-being at the core. Here&rsquo;s what your benefits package may include depending on your location and local legal requirements: Medical, Dental &amp; Vision Insurance, Flexible Time Off Program, Paid Holidays, Paid Parental Leave, Global Employee Assistance Program (EAP) and more!</div><div></div><div>
DataRobot Operating Principles:</div><ul><li>
Wow Our Customers</li><li>
Set High Standards</li><li>
Be Better Than Yesterday</li><li>
Be Rigorous</li><li>
Assume Positive Intent</li><li>
Have the Tough Conversations</li><li>
Be Better Together</li><li>
Debate, Decide, Commit</li><li>
Deliver Results</li><li>
Overcommunicate</li></ul><div><br>
Research shows that many women only apply to jobs when they meet 100% of the qualifications while many men apply to jobs when they meet 60%. At DataRobot we encourage ALL candidates, especially women, people of color, LGBTQ+ identifying people, differently abled, and other people from marginalized groups to apply to our jobs, even if you do not check every box. We&rsquo;d love to have a conversation with you and see if you might be a great fit.</div><div></div><div>
DataRobot is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. DataRobot is committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. Please see the United States Department of Labor&rsquo;s EEO poster and EEO poster supplement for additional information.</div><div></div><div>
All applicant data submitted is handled in accordance with our <div>Applicant Privacy Policy</div>.</div></div>

Senior AI Engineer - Professional Services - UAE

Explore 1097+ Mlops engineer in Dubai this October. Find roles across top companies and apply now to boost your career.