Machine Learning Engineer

Involved Solutions; Dubai; On-site; AED 350,000 - 520,000; Full time

Job summary

Involved Solutions partners with a fast-growing UAE technology business to hire a Machine Learning Engineer. The role focuses on applying cutting-edge ML to live production systems on serious GPU infrastructure.

You will fine-tune foundation models, build production agentic systems, optimise inference, and collaborate with product and engineering teams to turn business problems into measurable ML solutions that drive real outcomes.

Qualifications

• 5 years of hands-on ML engineering with production deployments
• Strong Python and PyTorch experience; Hugging Face Transformers
• Experience with LLMs in production, fine-tuning and evaluation
• Solid grounding in MLOps and cloud ML platforms on AWS, Azure or GCP
• Experience with model serving or a willingness to dive deep on it
• Exposure to multimodal models or distributed training is a plus

Responsibilities

• Fine-tune and adapt open-source and proprietary foundation models using techniques such as LoRA, QLoRA, DPO and RLHF, including multilingual and Arabic language use cases
• Build and ship production agentic systems with tool use, multi-step reasoning, retrieval and orchestration
• Optimise inference for latency and cost using quantisation, distillation and serving frameworks such as vLLM and TensorRT-LLM
• Design evaluation frameworks that measure model quality, safety and drift in live environments
• Build the MLOps backbone covering training pipelines, experiment tracking, model registries, CI/CD for ML and monitoring on GPU infrastructure
• Work with product and engineering teams to turn business problems into ML systems that ship and move the numbers

Skills

Python

PyTorch

Transformers

MLOps

Cloud ML

LLMs in prod

Model serving

GPU clusters

Multimodal models

Deployment

Job description

We have partnered with a fast-scaling technology business in the UAE to hire a Machine Learning Engineer. This is a team working on the hard end of applied AI: fine-tuning foundation models, building agentic systems that take real actions, and pushing inference to run faster and cheaper on serious GPU infrastructure. The models you build will be live in production, used by real customers and measured on commercial outcomes.

This role suits an engineer who wants to work with the current generation of ML tooling, not maintain a legacy pipeline. You will have the compute, the data and the autonomy to do it properly.

About the role:

• Fine-tune and adapt open-source and proprietary foundation models using techniques such as LoRA, QLoRA, DPO and RLHF, including multilingual and Arabic language use cases
• Build and ship production agentic systems with tool use, multi-step reasoning, retrieval and orchestration
• Optimise inference for latency and cost using quantisation, distillation and serving frameworks such as vLLM and TensorRT-LLM
• Design evaluation frameworks that measure model quality, safety and drift in live environments
• Build the MLOps backbone covering training pipelines, experiment tracking, model registries, CI/CD for ML and monitoring on GPU infrastructure
• Work with product and engineering teams to turn business problems into ML systems that ship and move the numbers

About you:

• 5 years of hands-on experience in machine learning engineering, with a track record of deploying models into production
• Strong Python and deep experience with PyTorch, plus Hugging Face Transformers or equivalent
• Practical experience with LLMs in production, including fine-tuning, RAG and evaluation
• Solid grounding in MLOps and cloud ML platforms on AWS, Azure or GCP
• Experience with model serving and inference optimisation, or a clear appetite to go deep on it
• Exposure to multimodal models, distributed training or GPU cluster work is a strong advantage
• Based in the UAE or willing to relocate