Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead the development and management of ML infrastructure for model deployment and maintenance.
- Company: AI71 is an innovative research team focused on impactful AI solutions in partnership with TII.
- Benefits: Enjoy full relocation support, competitive compensation, and access to advanced technology.
- Why this job: Join a collaborative environment driving real-world AI solutions that reshape industries.
- Qualifications: 5+ years in MLOps, strong Python skills, and experience with cloud services like AWS required.
- Other info: Work with cutting-edge ML models and contribute to high-impact projects.
The predicted salary is between 43200 - 72000 £ per year.
AI71 is an applied research team committed to building responsible and impactful AI agents that empower knowledge workers. In partnership with the Technology Innovation Institute (TII), we drive innovation through cutting-edge AI research and development. Our mission is to translate breakthroughs in machine learning into transformative products that reshape industries.
AI71 is seeking a Senior MLOps Engineer to lead the development and management of our infrastructure, designed for training, deploying, and maintaining ML models. This role plays a critical function in operationalizing state-of-the-art systems to ensure high-performance delivery across research and production environments.
The successful candidate will be responsible for designing and implementing infrastructure to support efficient model deployment, inference, monitoring, and retraining. This includes close collaboration with cross-functional teams to integrate machine learning models into scalable and secure production pipelines, enabling the delivery of real-time, data-driven solutions across various domains.
Key Responsibilities- Model Deployment: Lead the deployment and scaling of LLMs and other deep learning models using inference engines such as vLLM, Triton, or TGI, ensuring optimal performance and reliability.
- Pipeline Engineering: Design and maintain automated pipelines for model fine-tuning, evaluation, versioning, and continuous delivery using tools like MLflow, SageMaker Pipelines, or Kubeflow.
- Infrastructure Management: Architect and manage cloud-native, cost-effective infrastructure for machine learning workloads using AWS (SageMaker, EC2, EKS, Lambda) or equivalent platforms.
- Performance Optimization: Implement monitoring, logging, and optimization strategies to meet latency, throughput, and availability requirements across ML services.
- Collaboration: Work closely with ML researchers, data scientists, and engineers to support experimentation workflows, streamline deployment, and translate research prototypes into production-ready solutions.
- Automation & DevOps: Develop infrastructure-as-code (IaC) solutions to support repeatable, secure deployments and continuous integration/continuous delivery (CI/CD) for ML systems.
- Model Efficiency: Apply model optimization techniques such as quantization, pruning, and multi-GPU/distributed inference to enhance system performance and cost-efficiency.
- Professional Experience: Minimum 5 years of experience in MLOps, ML infrastructure, or machine learning engineering, with a strong record of managing end-to-end ML model lifecycles.
- Deployment Expertise: Proven experience in deploying large-scale models in production environments with advanced inference techniques.
- Cloud Proficiency: In-depth expertise in cloud services (preferably AWS), including infrastructure management, scaling, and cost optimization for ML workloads.
- Programming Skills: Strong programming proficiency in Python, with additional experience in C/C++ for performance-sensitive applications.
- Tooling Knowledge: Proficiency in MLOps frameworks such as MLflow, Kubeflow, or SageMaker Pipelines; familiarity with Docker and Kubernetes.
- Optimization Techniques: Hands-on experience with model performance optimization techniques and distributed training frameworks (e.g., DeepSpeed, FSDP, Accelerate).
- Educational Background: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Engineering, or a related technical field.
- Advanced Technology Stack: Work with some of the most capable large language models and cutting-edge ML infrastructure.
- High-Impact Work: Contribute directly to the deployment of AI solutions that deliver measurable business value across industries.
- Collaboration-Driven Environment: Engage with a high-performing, interdisciplinary team focused on continuous innovation.
- Robust Infrastructure: Access high-performance compute resources to support experimentation and scalable deployment.
- Relocation Package: Full support for relocation to Abu Dhabi, with a competitive compensation package and lifestyle benefits.
Senior MLOps Engineer [UAE Based] employer: AI71
Contact Detail:
AI71 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior MLOps Engineer [UAE Based]
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as MLflow, Kubeflow, and AWS services. Having hands-on experience or projects that showcase your skills with these tools can set you apart during interviews.
✨Tip Number 2
Network with professionals in the MLOps field, especially those who have experience working in the UAE or with AI71. Engaging with them on platforms like LinkedIn can provide insights into the company culture and expectations, which can be invaluable during your application process.
✨Tip Number 3
Prepare to discuss your previous experiences in deploying large-scale models and optimising performance. Be ready to share specific examples of challenges you faced and how you overcame them, as this will demonstrate your problem-solving abilities and technical expertise.
✨Tip Number 4
Showcase your collaborative skills by highlighting any past experiences where you worked closely with cross-functional teams. Emphasising your ability to communicate effectively with ML researchers and data scientists will illustrate your fit for the role at AI71.
We think you need these skills to ace Senior MLOps Engineer [UAE Based]
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps, ML infrastructure, and machine learning engineering. Focus on your achievements in deploying large-scale models and managing end-to-end ML lifecycles.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your understanding of the role. Mention specific projects or experiences that align with the responsibilities outlined in the job description.
Highlight Technical Skills: Clearly list your technical skills, especially in Python, AWS, and MLOps frameworks like MLflow or Kubeflow. Provide examples of how you've used these tools in previous roles to demonstrate your expertise.
Showcase Collaboration Experience: Emphasise your ability to work with cross-functional teams. Include examples of how you've collaborated with ML researchers, data scientists, and engineers to deliver successful projects.
How to prepare for a job interview at AI71
✨Showcase Your MLOps Expertise
Be prepared to discuss your experience in managing end-to-end ML model lifecycles. Highlight specific projects where you deployed large-scale models and the techniques you used, such as advanced inference methods.
✨Demonstrate Cloud Proficiency
Since the role requires expertise in cloud services, particularly AWS, be ready to talk about your experience with infrastructure management and cost optimisation for ML workloads. Mention any relevant certifications if you have them.
✨Familiarise Yourself with Their Tech Stack
Research AI71's technology stack, including tools like MLflow, Kubeflow, and SageMaker Pipelines. Be prepared to discuss how you've used these tools in past projects and how they can benefit the team.
✨Prepare for Collaboration Questions
As collaboration is key in this role, think of examples where you've worked closely with ML researchers, data scientists, or engineers. Be ready to explain how you facilitated communication and streamlined deployment processes.
