Production ML Engineer: Fast-Scale LLMs & MLOps (UAE)

Involved Solutions LTD.; Dubai; On-site; AED 180,000 - 300,000; Full time

Job summary

Involved Solutions LTD. is hiring a Machine Learning Engineer to join a fast-scaling UAE tech business. You will work on pushing production-grade ML systems, fine-tuning foundation models and building agentic workflows that operate at scale on GPU infrastructure.

You will collaborate with product and engineering teams to turn business challenges into live ML deployments, focusing on efficiency, cost, and measurable impact for customers in the region.

Qualifications

• 5+ years hands-on ML engineering experience with production deployments.
• Strong Python and PyTorch experience; familiarity with Hugging Face Transformers.
• Hands-on with LLMs in production, including fine-tuning and evaluation.
• Solid grounding in MLOps and cloud ML platforms (AWS/Azure/GCP).
• Experience with model serving and inference optimisation is a plus.

Responsibilities

• Fine-tune and adapt foundation models using LoRA, QLoRA, DPO and RLHF, including multilingual and Arabic use cases.
• Build production agentic systems with tool use, multi-step reasoning, retrieval and orchestration.
• Optimise inference for latency and cost using quantisation, distillation and serving frameworks like vLLM and TensorRT-LLM.
• Design evaluation frameworks to measure model quality, safety and drift in live environments.
• Develop the MLOps backbone covering training pipelines, experiment tracking and CI/CD for ML on GPU infrastructure.
• Collaborate with product and engineering teams to translate business problems into scalable ML solutions.

Skills

Python

PyTorch

LLMs in production

MLOps

Cloud platforms

Model serving

Tools

vLLM

TensorRT-LLM

LoRA

QLoRA

DPO

RLHF

Job description

Involved Solutions LTD. is hiring a Machine Learning Engineer to join a fast-scaling UAE tech business. You will work on pushing production-grade ML systems, fine-tuning foundation models and building agentic workflows that operate at scale on GPU infrastructure.

You will collaborate with product and engineering teams to turn business challenges into live ML deployments, focusing on efficiency, cost, and measurable impact for customers in the region.