AI/ML Solutions Engineer: Scale, Deploy & Observe

Apparel Group; Dubai; On-site; AED 350,000 - 700,000; Full time

Job summary

Apparel Group in Dubai invites a senior ML Engineer to translate business problems into scalable ML solutions, build end-to-end pipelines, optimize inference, and drive MLOps practices across teams.

The role requires strong Python, ML frameworks, data engineering skills, cloud experience, and collaboration with stakeholders to deploy production models with governance and cost-awareness.

Qualifications

• Bachelor's or Master's degree in CS/DS/AI or related field.
• Proven experience in designing, training, and deploying ML models and AI solutions.
• Strong programming skills in Python and familiarity with ML frameworks.
• Hands-on experience with MLOps tools and practices (Docker, Kubernetes, MLflow, CI/CD).
• Proficiency in data processing with Spark/Databricks and large datasets.
• Knowledge of model optimization techniques and production performance tuning.
• Familiarity with cloud platforms (Azure, AWS, GCP) and scalable architectures.
• Understanding of data governance, privacy standards, and compliance requirements.
• Strong analytical, problem-solving, and cross-functional collaboration skills.

Responsibilities

• Translate business problems into ML formulations and choose architectures with clear success metrics.
• Build end-to-end pipelines: feature extraction, training, tuning, packaging models as reproducible artifacts.
• Optimize inference for latency and throughput on CPU/GPU and monitor performance.
• Design experiments, APIs/SDKs, and provide clear documentation for stakeholders.

Skills

Python

ML frameworks

MLOps

Spark/Databricks

Cloud platforms

Data governance & privacy

Communication skills

Education

Bachelor's or Master's in CS/DS/AI

Tools

Docker

Kubernetes

MLflow

CI/CD pipelines

Job description

Apparel Group in Dubai invites a senior ML Engineer to translate business problems into scalable ML solutions, build end-to-end pipelines, optimize inference, and drive MLOps practices across teams.

The role requires strong Python, ML frameworks, data engineering skills, cloud experience, and collaboration with stakeholders to deploy production models with governance and cost-awareness.