ML/MLOps Engineer

About the opportunity

Location

• The role is located in Abu Dhabi, UAE and is explicitly on-site.

What you'll work on

• Design, develop, and productionize advanced machine learning and deep learning systems.
• Build and deploy LLM and NLP pipelines, including fine-tuning, semantic search, and retrieval-augmented generation.
• Deploy and scale large language models with vLLM, Triton, TGI, or similar inference frameworks.
• Develop machine learning solutions for forecasting, classification, anomaly detection, risk modeling, and optimization.
• Automate training, fine-tuning, evaluation, versioning, deployment, and retraining workflows.
• Containerize and orchestrate ML workloads with Docker and Kubernetes.
• Implement reproducible workflows with MLflow, Kubeflow, or equivalent tools.
• Design structured and unstructured data pipelines and optimize inference performance.
• Monitor production models for performance, drift, latency, throughput, and resource utilization.
• Build CI/CD and infrastructure automation for machine learning systems.
• Collaborate with researchers, data scientists, software engineers, and domain experts.
• Develop systems where security, reliability, explainability, and engineering rigor are critical.

What we're looking for

• 5+ years of experience in machine learning engineering, MLOps, ML infrastructure, or a closely related field.
• A strong track record of deploying machine learning models into production.
• Expert-level proficiency in Python.
• Strong knowledge of PyTorch, TensorFlow, Scikit-learn, or comparable modern ML frameworks.
• Experience with transformer-based architectures and modern NLP or generative AI ecosystems.
• 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 optimization, monitoring, lifecycle management, software engineering, and algorithmic fundamentals.
• Experience building scalable data and model pipelines.
• Ability to work across ambiguous technical problems and adapt ML techniques to specialized domains.

Advantageous experience

• Production deployment of large language models, RAG, semantic search, embeddings, or document intelligence.
• Experience with time-series forecasting, anomaly detection, or predictive modeling.
• Experience with AWS machine learning infrastructure such as SageMaker, EC2, or EKS.
• Experience with on-premise or highly secure ML deployment environments.
• Experience with distributed training or inference using DeepSpeed, FSDP, or Accelerate.
• Experience with infrastructure as code, ML-focused CI/CD, or C/C++ for performance-sensitive systems.

Why consider this role

• The role offers work on technically challenging AI systems that address meaningful real-world problems.
• The team provides access to engineering talent, modern AI infrastructure, substantial compute resources, LLMs, traditional machine learning, and large-scale production AI systems.