Machine Learning Engineer

Discovered MENA; Abu Dhabi; On-site; AED 90,000 - 120,000; Full time

Job summary

A leading company in AI solutions is seeking an MLOps Engineer to take charge of deploying and optimizing ML models in Abu Dhabi. The ideal candidate will manage full ML lifecycle, collaborate across teams, and utilize cloud technologies to enhance model performance. This role offers full relocation support and the opportunity to work with cutting-edge technologies in a dynamic environment.

Qualifications

• 5+ years of hands-on experience in MLOps and ML infrastructure.
• Proficient in Python and familiar with C/C++.
• Demonstrated experience deploying large-scale ML models.

Responsibilities

• Oversee deployment of large language models and deep learning systems.
• Build and manage automated pipelines for model fine-tuning.
• Design cloud-native infrastructure for ML workloads.

Skills

ML lifecycle management

Cloud infrastructure management

Model optimization

Team collaboration

Infrastructure as Code

Performance optimization

Proficient in Python

C/C++ programming

Education

Bachelor's or Master’s degree in Computer Science

Degree in Machine Learning

Degree in Data Engineering

Tools

MLflow

Kubeflow

Docker

Kubernetes

Job description

MLOps Engineer

Onsite in Abu Dhabi, full relocation provided

Key Responsibilities

• Model Deployment: Oversee the deployment and scaling of large language models (LLMs) and other deep learning systems using modern inference engines such as vLLM, Triton, or TGI, with a focus on reliability and performance.
• Pipeline Engineering: Build and manage automated pipelines for model fine-tuning, evaluation, versioning, and continuous delivery using platforms like MLflow, Kubeflow, or comparable tooling.
• Infrastructure Management: Design and maintain cloud-native infrastructure for ML workloads, leveraging services from major cloud providers (e.g., EC2, Kubernetes, serverless functions, managed ML services).
• Performance Optimization: Implement robust monitoring and logging strategies, ensuring low-latency, high-availability systems that meet production-grade performance metrics.
• Cross-Functional Collaboration: Partner with data scientists, ML researchers, and software engineers to support experimentation workflows and ensure research-to-production continuity.
• DevOps & Automation: Create infrastructure-as-code (IaC) solutions and CI/CD pipelines for repeatable, secure deployments of ML systems.
• Model Optimization: Apply techniques such as quantization, pruning, and distributed inference to maximize performance while minimizing computational costs.

Qualifications

• Experience: 5+ years of hands-on experience in MLOps, ML infrastructure, or related engineering roles, with a strong track record in managing the full ML lifecycle.
• Deployment Expertise: Demonstrated experience deploying large-scale ML models with advanced inference and optimization practices.
• Cloud Infrastructure: Deep understanding of cloud platforms (preferably AWS or equivalents), including scalable architecture design and cost-efficient compute management.
• Programming: Proficient in Python, with experience in C/C++ for performance-critical applications.
• Tooling: Well-versed in MLOps tools such as MLflow, Kubeflow, or SageMaker Pipelines; strong working knowledge of Docker, Kubernetes, and distributed systems.
• Optimization: Familiarity with tools and frameworks for distributed training and inference such as DeepSpeed, FSDP, or Accelerate.
• Education: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Engineering, or a related discipline.