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Halian

Senior MLOps Engineer - Humanoid RoboticsLocation: Abu Dhabi, UAEMust-Have Skills:AWS infrastructure (S3, EC2, ECR, CodeCommit) SageMaker and cloud-based model training Experience training large-scale models (3B+ parameters) PyTorch (DDP, FSDP) for distributed training Docker for SageMaker and general containerization Experiment tracking (wandb, SageMaker Logger) Strong understanding of Neural Networks, particularly transformers Nice-to-Have Skills:Experience with Ray or Accelerate for distributed computing Experience with SLURM job scheduling Knowledge of model quantization and distillation techniques Experience in orchestrating multiple ML training jobs Who We're Looking For:You are proactive and don t wait for someone to tell you what to do. You are comfortable putting in extra hours when deadlines require it. You thrive in fast-paced, high-performance teams where results matter. You don t just execute you think, optimize, and solve problems independently MLOps Engineer in Abu Dhabi, United Arab Emirates

MLOps Engineer (m/f/d)

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

INFRA ASSURE

Job PurposeManages machine learning pipelines, automates deployment, and ensures ongoing monitoring and maintenance of AI models.Key Responsibilities&middot; Develop and maintain CI/CD pipelines for ML models.&middot; Automate model training, testing, deployment, and monitoring.&middot; Implement tools for drift detection, model performance tracking, and retraining.&middot; Ensure reliability, scalability, and auditability of ML infrastructure.&middot; Collaborate with engineers and data scientists to ensure seamless deployment.Minimum QualificationsEducation: Bachelor of Computer Science, Master's in AI or Data Science is a plus.Experience: 3&ndash;5 yearsKey Competency RequirementsTechnical Skills:&middot; Expertise in MLOps tools (MLflow, TFX, Kubeflow).&middot; Experience with Kubernetes, Docker, and cloud platforms.Operational Skills:&middot; Automation of model lifecycle and monitoring workflows.&middot; Knowledge of version control and DevOps practices.Soft Skills:&middot; Attention to detail and proactive monitoring.&middot; Strong documentation and support capabilities.Job Type: Full-timeApplication Question(s):<ul><li>What is your current notice period or earliest joining date?</li><li>What are your salary expectations for this role?</li><li>Current location, nationality, and current job title/role?</li><li>Are you willing to work remotely ?</li><li>Have you worked with CI/CD pipelines specifically for machine learning model deployment?</li><li>Are you experienced with Kubernetes, Docker, and cloud platforms (AWS / Azure / GCP)?</li><li>Total years of experience in MLOps / ML Engineering?</li></ul>Education:<ul><li>Bachelor's (Preferred)</li></ul>Experience:<ul><li>MLOps tools such as MLflow, Kubeflow, TFX, or Airflow: 5 years (Preferred)</li></ul>Language:<ul><li>Arabic and English (Preferred)</li></ul>Location:<ul><li>Dubai (Preferred)</li></ul>

AI/ML Operations (MLOps)

Etihad Credit Bureau

<div>
 
 
 JOB DESCRIPTION SUMMARY
 
 
 
 
 
 
 The Machine Learning Engineer role will specialise in maintaining scoring models, production system maintenance and high-availability operations. You'll orchestrate and maintain our AzureML/Databricks-based scoring engine, ensure 99.99% uptime for production models, perform emergency fixes, and manage QA/UAT processes. This role partners with data scientists to operationalize models and data engineers to ensure efficient data flows.
 
 
 
 
 
 
 KEY DUTIES &amp; RESPONSIBILITIES
 
 
 <ul>
 <li>
 
 Production Model Maintenance:
 
 Monitor, troubleshoot, and rectify issues in deployed credit scoring models (e.g., score drift, feature misalignment, output anomalies).
 </li>
 <li>
 
 Platform Orchestration:
 
 Manage AzureML pipelines &amp; Databricks workflows for model retraining, batch scoring, and real-time inference.
 </li>
 <li>
 
 High-Availability Engineering:
 
 Ensure 24/7 uptime of scoring APIs serving banking clients; implement failover systems and load balancing.
 </li>
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 Release Management:
 
 Oversee QA/UAT processes for model updates including back-testing, shadow deployments, and canary releases.
 </li>
 <li>
 
 Model Governance:
 
 Maintain audit trails for model versions, inputs/outputs, and performance metrics. Support Compliance and Audit with creation of logs when requested.
 </li>
 <li>
 
 Incident Response:
 
 Lead troubleshooting of scoring engine failures with SLAs for financial institution clients.
 </li>
 <li>
 
 Infrastructure Optimization:
 
 Tune AzureML/Databricks clusters for cost-performance efficiency at scale.
 </li>
 <li>
 
 Vendor Management:
 
 Ensure vendor support is completing work as per scope and SLAs. Rectifying any vendor delivery issues.
 </li>
 </ul>
 
 
 
 
 
 EDUCATION &amp; SKILLS
 
 
 <ol>
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 Educated with at least bachelor&rsquo;s degree or equivalent in related field
 </li>
 <li>
 Education specialization or master&rsquo;s degree in computer science, Software Engineering
 </li>
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 Proficient in English
 </li>
 <li>
 Preferred proficiency in Arabic
 </li>
 <li>
 In-depth knowledge of React Native and Next JS and related modules, components and libraries
 </li>
 <li>
 Preferred In-depth knowledge of SiteCore or experience integrating with SiteCore
 </li>
 </ol>
 
 
 
 
 
 EXPERIENCE &amp; KNOWLEDGE
 
 
 <ol>
 <li>
 Bachelor's/Master's in Computer Science, Engineering, Data Science, or related field
 </li>
 <li>
 2+ years in Data Science or Software Engineering experience
 </li>
 <li>
 2+ years production ML operations experience, MLOps lifecycle management, including monitoring, retraining, and model versioning.
 </li>
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 Strong problem-solving skills and the ability to resolve issues efficiently.
 </li>
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 CI/CD for ML systems using tools such as Azure DevOps, GitHub Actions, MLflow, and other similar tools
 </li>
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 Ability to adapt ML workflows across different cloud environments (Azure, AWS, GCP) as needed.
 </li>
 </ol>
 
 
 
 
 
 ABILITIES &amp; SPECIFIC REQUIREMENTS
 
 
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 Practical experience in cloud-based ML platforms such as AzureML, Databricks, or equivalent (e.g., SageMaker, Vertex AI), with a preference for Azure.
 </li>
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 Python/PySpark for model debugging and patching. Working knowledge of Scikit-Learn and NumPy
 </li>
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 Deep understanding of credit scoring systems: feature engineering, scorecard interpretation, and output validation
 </li>
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 Credit bureau data structures (tradelines, inquiries, public records)
 </li>
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 Model risk management (MRM) standards
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 Azure Solutions Architect or MLOps certifications
 </li>
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 Experience with financial services-grade SLAs (99.9% uptime) and outage management
 </li>
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 Knowledge of containerization (Docker, Kubernetes, AKS, or similar orchestration tools)
 </li>
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 Practical experience with Azure Data Factory (ADF) and Azure Data Lake Storage (ADLS) / Azure Blob Storage
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 Knowledge of new and upcoming AI tools
 </li>
 <li>
 This is contained within the earlier requirement of python. If the plan is for this resource
 </li>
 <li>
 Excellent written and verbal communication skills English
 </li>
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 Strong interpersonal skills with the ability to engage and build relationships
 </li>
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 Crisis management under pressure
 </li>
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 Cross-functional collaboration with data science/risk teams
 </li>
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 Strong organizational skills with the ability to manage multiple tasks and projects simultaneously.
 </li>
 <li>
 Ability to develop and document procedures, roles, and guidelines.
 </li>
 <li>
 
 Security and Compliance
 
 Especially in financial services, mention data security, PII handling, and compliance with regulations
 </li>
 </ol>
</div>

Machine Learning Engineer (Contract - For Immediate Hire)

Client of S&K HR Consulting

Our client is a leading software solutions organisation, who specialise in empowering businesses with innovative technology solutions by providing SaaS solutions tailored to business needs. 
They are currently hiring for an AI Engineer to own the end-to-end lifecycle of AI features, from data ingestion and RAG setup to fine-tuning, evaluation, deployment, and continuous improvement, so they can ship reliable, cost-effective AI products.
 
 Location: On-site (Dubai, UAE) 
 Type: Full-time 
 
 What You'll Do <ul> <li> Design and implement RAG pipelines (chunking, embeddings, vector stores, retrieval strategies) using tools like Ollama, LangChain, LlamaIndex, or equivalent. </li> <li> Stand up local and cloud LLM orchestration (prompt routing, tool use, function calling, guards) with strong observability. </li> <li> Run fine-tuning / LoRA / adapters; build data re-entry loops to capture outputs and feedback for secondary retraining. </li> <li> Create robust prompt engineering patterns (templates, guards, evals, versioning) and latency/cost controls. </li> <li> Build evaluation suites (RAGAS, custom golden sets, offline + online A/B tests) and quality dashboards. </li> <li> Productionize models with MLOps best practices (CI/CD, model registries, feature stores, experiment tracking). </li> <li> Ensure privacy, safety, and compliance (PII handling, red-teaming, prompt injection defenses, content filters). </li> <li> Collaborate with PM/Design/Eng to scope features and deliver increments quickly. </li> </ul>
 
 Must-Have Qualifications <ul> <li>3+ years shipping ML/AI systems, including at least one production RAG deployment. </li> <li> Hands-on with LangChain/Ollama (or similar), vector DBs (Pinecone, Weaviate, Milvus, pgvector), and embedding models. </li> <li> Experience with fine-tuning (HF Transformers, PEFT/LoRA) and dataset curation/cleaning. </li> <li> Strong Python skills; solid grasp of APIs, microservices, and async patterns. </li> <li> Familiar with LLM evals, metrics (precision@k, faithfulness, groundedness), and cost/perf tuning. </li> <li> Cloud/containerization: Docker, any of AWS/GCP/Azure, basic GPU/accelerator know-how. </li> </ul>
 
 Nice to Have <ul> <li> Llama 3/4, Mistral, OpenAI/Anthropic APIs; Guardrails/Gandalf; Weights &amp; Biases or MLflow. </li> <li> Feature stores, Kafka, Airflow; security hardening and secret management. </li> <li> Basic front-end to prototype admin/eval tools (React/Next.js). </li> </ul>
 
 Success Metrics <ul> <li> RAG answer quality (e.g., &gt;85% groundedness on eval set) and unit cost over time. </li> <li> P50 latency within target; model incidence rate (hallucinations, jailbreaks) MoM. </li> <li> Time-to-ship for new datasets/features.</li> </ul>

AI Engineer

Explore 1290+ Mlops engineer in Dubai United Arab Emirates this November. Find roles across top companies and apply now to boost your career.