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- Tasks: Lead the development and management of ML infrastructure for deploying and maintaining models.
- Company: Join AI71, a pioneering team at TII focused on impactful AI research and innovation.
- Benefits: Enjoy full relocation support to Abu Dhabi and a competitive compensation package.
- Why this job: Be part of transformative AI solutions and work with cutting-edge technology in a collaborative environment.
- Qualifications: 5+ years in MLOps or machine learning engineering, with strong Python skills and cloud expertise.
- Other info: Access high-performance resources and contribute to real-time, data-driven solutions.
The predicted salary is between 54000 - 84000 £ per year.
AI71 is an applied research team committed to building responsible and impactful AI. We drive innovation through cutting-edge AI research and development. We are seeking a Senior MLOps Engineer to lead the development and management of our infrastructure, designed for training, deploying, and maintaining ML models. This role is critical in operationalizing state-of-the-art systems to ensure high-performance infrastructure to support efficient model deployment, inference, monitoring, and machine learning models into scalable and secure production pipelines, enabling the delivery of real-time, data-driven solutions across various domains.
Model Deployment: Lead the deployment and scaling of LLMs and other deep learning models for optimal performance and reliability.
- Infrastructure for machine learning workloads using AWS (SageMaker, EC2, EKS).
- Performance Optimization: Implement monitoring, logging, and optimization strategies to meet latency, throughput, and availability requirements across ML.
- Collaboration: Work closely with ML researchers, data scientists, and engineers to support experimentation workflows, streamline deployment, and translate research.
- Automation & DevOps: Develop infrastructure-as-code (IaC) solutions to support repeatable, secure deployments and continuous integration/continuous delivery (CI/CD) for ML systems.
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: 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.
- Hands-on experience with model performance optimization techniques and distributed training frameworks.
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 enhance system performance.
- 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 and Machine Learning Engineer employer: AI71
Contact Detail:
AI71 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior MLOps and Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in MLOps and machine learning infrastructure. Being well-versed in tools like AWS SageMaker, EC2, and EKS will not only boost your confidence but also demonstrate your commitment to staying current in this fast-evolving field.
✨Tip Number 2
Network with professionals in the MLOps community, especially those who have experience working with large language models. Engaging in discussions on platforms like LinkedIn or attending relevant meetups can provide insights and connections that may lead to opportunities at StudySmarter.
✨Tip Number 3
Showcase your hands-on experience with model performance optimisation techniques and distributed training frameworks. Prepare to discuss specific projects where you've successfully implemented these strategies, as practical examples can set you apart from other candidates.
✨Tip Number 4
Be ready to demonstrate your programming skills, particularly in Python and C/C++. Consider preparing a small project or code snippet that highlights your ability to optimise ML workloads, as this can be a great conversation starter during interviews.
We think you need these skills to ace Senior MLOps and Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps and machine learning engineering. Focus on your expertise with cloud services, particularly AWS, and any specific projects that demonstrate your skills in deploying and managing ML models.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your understanding of the role. Mention how your background aligns with the responsibilities of leading infrastructure development and collaboration with ML researchers.
Highlight Technical Skills: In your application, emphasise your programming proficiency in Python and any experience with C/C++. Include details about your familiarity with performance optimization techniques and distributed training frameworks to stand out.
Showcase Relevant Projects: Include examples of past projects where you successfully implemented MLOps practices or developed scalable ML solutions. This will demonstrate your hands-on experience and ability to contribute to high-impact work.
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 led the deployment and scaling of machine learning models, particularly using AWS services like SageMaker or EC2.
✨Demonstrate Collaboration Skills
Since the role involves working closely with ML researchers and data scientists, share examples of how you've successfully collaborated in cross-functional teams. Emphasise your ability to streamline deployment processes and support experimentation workflows.
✨Highlight Programming Proficiency
Make sure to mention your programming skills, especially in Python and C/C++. Be ready to discuss any performance-sensitive applications you've developed and how your coding skills have contributed to optimising ML infrastructure.
✨Discuss Infrastructure-as-Code Experience
Talk about your experience with infrastructure-as-code (IaC) solutions and CI/CD for ML systems. Provide examples of how you've implemented these strategies to ensure secure and repeatable deployments, which is crucial for the role.
