Machine Learning Engineer - (Senior to Staff level)

Quantum Talent Group; Abu Dhabi Emirate; On-site; AED 420,000 - 600,000; Full time

Job summary

Quantum Talent Group is recruiting a Senior Machine Learning specialist to bridge model development and production in Abu Dhabi. You will own significant parts of the end-to-end ML lifecycle, from development to deployment, with emphasis on applied AI research, ML engineering and production infrastructure.

You will design, deploy and productionise advanced ML systems, including LLM/NLP pipelines, with attention to performance, reliability and security.

Qualifications

• 5+ years of experience in ML Engineering, MLOps or ML infrastructure.
• Expert-level proficiency in Python.
• Experience deploying ML models into production.
• Strong knowledge of PyTorch, TensorFlow and Scikit-learn.
• Experience with transformer-based NLP/GenAI ecosystems (Hugging Face, Llama-family, GPT-style models).
• Hands-on with Docker, Kubernetes and production ML orchestration.
• Experience with MLflow, Kubeflow, SageMaker Pipelines or similar.
• Understanding of model serving, inference optimization, monitoring and lifecycle management.
• Experience building scalable data and model pipelines.
• Solid software engineering fundamentals.

Responsibilities

• Design, develop and productionise advanced ML and deep learning systems.
• Build and deploy LLM and NLP pipelines, including fine-tuning, semantic search and RAG.
• Deploy and scale large language models using modern inference frameworks.
• Develop ML solutions across forecasting, classification, anomaly detection, risk modelling and optimisation.
• Build automated pipelines for training, fine-tuning, evaluation, versioning, deployment and retraining.
• Containerise and orchestrate ML workloads using Docker and Kubernetes.
• Implement reproducible ML workflows using MLflow, Kubeflow or equivalent tooling.
• Design data processing pipelines for structured and unstructured data.
• Improve inference performance via quantisation, distillation, pruning and multi-GPU inference.
• Monitor production models for performance, drift, latency and resource utilisation.
• Build CI/CD and infra automation for ML systems.
• Collaborate with researchers, data scientists, software engineers and domain experts.

Skills

Senior ML Engineer

Python

PyTorch

TensorFlow

Scikit-learn

Transformers

NLP/GenAI

Docker

Kubernetes

MLflow

Kubeflow

SageMaker Pipelines

Model serving

CI/CD for ML

Software engineering fundamentals

Tools

Docker

Kubernetes

MLflow

Kubeflow

SageMaker Pipelines

Job description

Senior Machine Learning (Senior to Staff level)

About the opportunity

We are hiring for a rapidly growing advanced AI organisation based in Abu Dhabi, building and deploying sophisticated machine learning and generative AI systems for complex, high-impact real-world applications.

The team operates at the intersection of applied AI research, machine learning engineering and production infrastructure, taking models from experimentation through to scalable, secure production environments.

We are looking for a Senior Machine Learning who can bridge the gap between model development and production. Depending on your background, you may lean more heavily toward ML engineering, GenAI or ML infrastructure, but you should be comfortable owning significant parts of the end-to-end machine learning lifecycle.

What you'll work on

• Design, develop and productionise advanced machine learning and deep learning systems
• Build and deploy LLM and NLP pipelines, including fine-tuning, semantic search and Retrieval-Augmented Generation (RAG)
• Deploy and scale large language models using modern inference frameworks such as vLLM, Triton or TGI
• Develop machine learning solutions across areas such as forecasting, classification, anomaly detection, risk modelling and optimisation
• Build automated pipelines for training, fine-tuning, evaluation, versioning, deployment and retraining
• Containerise and orchestrate ML workloads using Docker and Kubernetes
• Implement reproducible ML workflows using platforms such as MLflow, Kubeflow or equivalent tooling
• Design data processing pipelines covering both structured and unstructured data
• Improve inference performance through techniques such as quantisation, distillation, pruning and distributed or multi-GPU inference
• Monitor production models for performance, drift, latency, throughput and resource utilisation
• Build reliable CI/CD and infrastructure automation for machine learning systems
• Work closely with researchers, data scientists, software engineers and domain experts to transform prototypes into robust production solutions
• Develop systems that operate within environments where security, reliability, explainability and engineering rigour are critical

What we're looking for

• 5+ years of experience in Machine Learning Engineering, MLOps, ML Infrastructure or a closely related field
• Strong track record of deploying machine learning models into production
• Expert-level proficiency in Python
• Strong knowledge of modern ML/deep learning frameworks such as PyTorch, TensorFlow and Scikit-learn
• Experience with transformer-based architectures and modern NLP/GenAI ecosystems such as Hugging Face, Llama-family models, GPT-style models or equivalent
• Hands‑on experience with Docker, Kubernetes and production ML orchestration
• Experience with MLflow, Kubeflow, SageMaker Pipelines or comparable MLOps platforms
• Strong understanding of model serving, inference optimisation, monitoring and lifecycle management
• Experience building scalable data and model pipelines
• Strong understanding of software engineering and algorithmic fundamentals
• Ability to work across ambiguous technical problems and quickly adapt ML techniques to specialised domains

Any of the following would be advantageous:

• Production deployment of large language models
• RAG, semantic search, embeddings or document intelligence
• Fine‑tuning and optimisation of transformer models
• Time‑series forecasting, anomaly detection or predictive modelling
• AWS machine learning infrastructure, including SageMaker, EC2 or EKS
• On-premise or highly secure ML deployment environments
• Distributed training or inference using tools such as DeepSpeed, FSDP or Accelerate
• Infrastructure as Code and ML-focused CI/CD
• C/C++ experience for performance‑sensitive systems

Why consider this role?

This is an opportunity to work on technically challenging AI systems where models move beyond prototypes and are deployed against meaningful, real-world problems.

You'll have access to strong engineering talent, modern AI infrastructure and substantial compute resources while working across LLMs, traditional machine learning and large-scale production AI systems.