Production-Ready AI/ML Engineer

Apparel Group

Dubai

On-site

AED 360,000 - 520,000

Full time

14 days+
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Job summary

Apparel Group is seeking an experienced ML Engineer to design, train, and deploy production ML models and AI solutions. You will collaborate across teams to build robust pipelines, integrate models with business rules, and ensure observability and governance of data and models.

The role emphasizes end-to-end ML lifecycle, including experimentation, deployment, and cost-aware optimization in a production environment.

Qualifications

  • Bachelor’s or Master’s degree in CS/DS/AI/ML required.
  • Proven experience designing, training, deploying ML models and AI solutions.
  • Strong Python programming and familiarity with ML frameworks.

Responsibilities

  • Model & Solution Engineering.
  • Translate business problems into ML formulations; select architectures with clear success metrics.
  • Build end-to-end pipelines: feature extraction, training, hyperparameter tuning, packaging models as artifacts.
  • Optimize inference for latency and throughput on CPU/GPU.
  • Evaluate beyond accuracy: calibration, fairness, cost-sensitive metrics, PR/ROC under imbalance.
  • MLOps, Deployment & Observability: model versioning, lineage, experimentation, canaries.
  • Build real-time and batch inference services; integrate with message buses and vector DBs.
  • Monitor data drift, performance regression, and cost observability.
  • Create alerting and autoscaling policies tied to SLAs; incident runbooks for models.
  • Data Engineering, Quality & Governance: ETL/ELT, data contracts, schema evolution.

Skills

Python
TensorFlow
PyTorch
Scikit-learn
Docker
Kubernetes
MLflow
Spark
Databricks
Azure
AWS
GCP
CI/CD
Data governance
Communication

Education

Bachelor’s or Master’s degree in CS/DS/AI/ML

Tools

Docker
Kubernetes
MLflow
Databricks
Spark
Azure
AWS
GCP
CI/CD

Job description

Apparel Group is seeking an experienced ML Engineer to design, train, and deploy production ML models and AI solutions. You will collaborate across teams to build robust pipelines, integrate models with business rules, and ensure observability and governance of data and models.

The role emphasizes end-to-end ML lifecycle, including experimentation, deployment, and cost-aware optimization in a production environment.

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