Machine Learning Infrastructure Engineer [UAE Based]

AI71
London
6 days ago
Create job alert

Job Title: ML Infrastructure Senior Engineer

Location: Abu Dhabi, United Arab Emirates [Full relocation package provided]



Job Overview

We are seeking a skilled ML Infrastructure Engineer to join our growing AI/ML platform team. This role is ideal for someone passionate about large-scale machine learning systems and has hands-on experience deploying LLMs/SLMs using advanced inference engines like vLLM. You will play a critical role in designing, deploying, optimizing, and managing ML models and the infrastructure around them—both for inference, fine-tuning and continued pre-training.


Key Responsibilities

· Deploy large-scale or small language models (LLMs/SLMs) using inference engines (e.g., vLLM, Triton, etc.).

· Collaborate with research and data science teams to fine-tune models or build automated fine-tuning pipelines.

· Extend inference-level capabilities by integrating advanced features such as multi-modality, real-time inferencing, model quantization, and tool-calling.

· Evaluate and recommend optimal hardware configurations (GPU, CPU, RAM) based on model size and workload patterns.

· Build, test, and optimize LLMs Inference for consistent model deployment.

· Implement and maintain infrastructure-as-code to manage scalable, secure, and elastic cloud-based ML environments.

· Ensure seamless orchestration of the MLOps lifecycle, including experiment tracking, model registry, deployment automation, and monitoring.

· Manage ML model lifecycle on AWS (preferred) or other cloud platforms.

· Understand LLM architecture fundamentals to design efficient scalability strategies for both inference and fine-tuning processes.


Required Skills


Core Skills:

· Proven experience deploying LLMs or SLMs using inference engines like vLLM, TGI, or similar.

· Experience in fine-tuning language models or creating automated pipelines for model training and evaluation.

· Deep understanding of LLM architecture fundamentals (e.g., attention mechanisms, transformer layers) and how they influence infrastructure scalability and optimization.

· Strong understanding of hardware-resource alignment for ML inference and training.

Technical Proficiency:

· Programming experience in Python and C/C++, especially for inference optimization.

· Solid understanding of the end-to-end MLOps lifecycle and related tools.

· Experience with containerization, image building, and deployment (e.g., Docker, Kubernetes optional).

Cloud & Infrastructure:

· Hands-on experience with AWS services for ML workloads (SageMaker, EC2, EKS, etc.) or equivalent services in Azure/GCP.

· Ability to manage cloud infrastructure to ensure high availability, scalability, and cost efficiency.


Nice-to-Have

· Experience with ML orchestration platforms like MLflow, SageMaker Pipelines, Kubeflow, or similar.

· Familiarity with model quantization, pruning, or other performance optimization techniques.

· Exposure to distributed training frameworks like Unsloth, DeepSpeed, Accelerate, or FSDP.

Related Jobs

View all jobs

Senior Data Engineer, EMEA

Senior Data Scientist

Senior MLops (Full Stack) Engineer | London | Foundation Models

Senior MLops (Full Stack) Engineer | London | Foundation Models

Machine Learning Engineer (Visual Specialist)

Machine Learning Engineer (London Area)

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Rural-Remote Machine Learning Jobs: Finding Balance Beyond the Big Cities

Over the past decade, machine learning (ML) has transformed from a niche research domain into a pervasive technology underpinning everything from recommendation systems and voice assistants to financial forecasting and autonomous vehicles. Historically, the UK’s major tech hubs—particularly London—have been magnets for top ML talent and corporate headquarters. However, remote work has become mainstream, and many ML professionals are realising they can excel in their field while living far beyond the city limits. At MachineLearningJobs.co.uk, we’ve observed a growing interest in positions that allow for a rural lifestyle or a coastal environment, often reflected in search terms like “ML remote countryside” or “tech jobs by the sea.” This surge is no coincidence. Flexible work policies, better rural broadband, and the nature of machine learning tasks—much of which can be done through cloud platforms—are bringing new opportunities to those who wish to swap urban hustle for fresh air and scenic views. Whether you’re a data scientist, ML engineer, researcher, or product manager, a rural or seaside move could reinvigorate your work-life balance. In this article, we’ll unpack why rural-remote ML jobs are on the rise, how you can navigate the challenges of leaving the city, and what you need to do to thrive in a machine learning career beyond the M25. If you’ve dreamt of looking up from your laptop to rolling fields or ocean waves, keep reading—your rural ML role might be closer than you think.

Quantum-Enhanced Machine Learning—Propelling AI into the Next Frontier

Machine learning (ML) has revolutionised how we interpret data, build predictive models, and create intelligent applications. From recommendation engines and self-driving cars to advanced genomics and natural language processing, ML solutions are integral to nearly every corner of modern life. However, as data complexity and model size continue to skyrocket, the computational demands placed on ML systems grow in tandem—often pushing even high-performance classical computers to their limits. In recent years, quantum computing has emerged as a tantalising solution to these challenges. Unlike traditional digital systems, quantum computers exploit quantum mechanics—superposition and entanglement—to process information in ways that defy conventional logic. As these machines mature, they promise exponential speed-ups for certain tasks, potentially reshaping how we approach AI and data-intensive challenges. What does this mean for machine learning? Enter quantum-enhanced ML, a new frontier where quantum processors and classical ML frameworks unite to accelerate model training, tackle high-dimensional data, and solve complex optimisation tasks more efficiently. In this article, we will: Unpack the current state of machine learning, highlighting key bottlenecks. Provide a concise overview of quantum computing—why it’s radical and how it differs from classical technology. Examine potential breakthroughs in quantum-enhanced ML, including real-world use cases and technical approaches. Explore the roles and skill sets that will define this quantum-AI era, with guidance on how to prepare. Discuss the roadblocks (like hardware maturity and ethical concerns) and how they might be addressed in the years to come. If you’re a machine learning engineer, data scientist, or simply an AI enthusiast fascinated by the next wave of computational innovation, read on—quantum computing could become an integral part of your future toolkit, opening up job opportunities and reimagining what ML can achieve.

Machine Learning Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Machine learning (ML) has become the beating heart of modern tech innovation, powering breakthroughs in healthcare, finance, cybersecurity, robotics, and more. Across the United Kingdom, this surge in ML-driven solutions is fueling the success of countless start-ups—and spurring demand for talented machine learning engineers, data scientists, and related professionals. If you’re eager to join a high-growth ML company or simply want to keep tabs on the latest trends, this Q3 2025 Investment Tracker will guide you through the newly funded UK start-ups pushing the boundaries of ML. In this article, we’ll highlight key developments from Q3 2025, delve into the most promising newly funded ventures, and shed light on the machine learning roles they’re urgently seeking to fill. Plus, we’ll show you how to connect with these employers via MachineLearningJobs.co.uk, a dedicated platform for ML job seekers. Let’s dive in!