Machine Learning Infrastructure Engineer [UAE Based]

JR United Kingdom; Slough; On-site; GBP 60,000 - 90,000; Full time

Job summary

A leading technology company seeks a Machine Learning Infrastructure Engineer based in Slough to join their AI/ML platform team. This position involves designing and managing large-scale machine learning systems, optimizing infrastructure, and deploying advanced models. Ideal candidates will have strong experience in LLM deployments, programming skills in Python and C/C++, and familiarity with AWS services for effective MLOps management.

Qualifications

• Proven experience deploying LLMs or SLMs with inference engines.
• Deep understanding of LLM architecture.
• Strong programming experience in Python and C/C++.

Responsibilities

• Deploy large-scale language models using advanced inference engines.
• Collaborate with data science teams on model optimization.
• Manage ML model lifecycle on AWS and ensure robust MLOps.

Skills

Deployment of LLMs or SLMs

Fine-tuning language models

Understanding of LLM architecture

Programming in Python and C/C++

MLOps lifecycle understanding

Containerization (Docker, Kubernetes)

AWS Services for ML

Job description

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Machine Learning Infrastructure Engineer [UAE Based], slough

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Client:

AI71

Location:

slough, United Kingdom

Job Category:

Other

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EU work permit required:

Yes

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Job Views:

5

Posted:

31.05.2025

Expiry Date:

15.07.2025

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Job Description:

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).

· 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.