AI ML Engineer

Apparel Group; Dubai; On-site; AED 360,000 - 520,000; Full time

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

Job Purpose:

Focuses on creating advanced machine learning models and AI-driven applications to solve complex business challenges. This

position ensures the development of robust, scalable, and efficient systems for real-world deployment. The engineer will collaborate

across teams to integrate AI solutions into production environments seamlessly.

Key responsibilities

• Model & Solution Engineering

• Translate business problems into ML formulations; select suitable architectures (e.g., gradient boosting, transformers) with clear success metrics.

• Build end-to-end pipelines: feature extraction, training, hyperparameter tuning, and packaging models as reproducible artifacts.

• Optimize inference (quantization, distillation, mixed precision) for latency and throughput on CPU/GPU.

• Conduct evaluation beyond accuracy (calibration, fairness, cost-sensitive metrics, PR/ROC under imbalance).

• MLOps, Deployment & Observability

• Implement model versioning, lineage, and experiment tracking; manage rollbacks and canary releases.

• Build real-time and batch inference services; integrate with message buses and vector databases.

• Monitor for schema checks, data drift, performance regression, and cost observability.

• Create alerting and autoscaling policies tied to SLAs, maintain incident runbooks for model services

• Data Engineering, Quality & Governance

• Design data contracts; implement ETL/ELT pipelines (e.g., Spark/Databricks) with testing and backfills.

• Enforce data quality gates and schema evolution strategies to prevent mismatches.

• Apply privacy-by-design: PII handling, tokenization, and secure secrets management.

• Collaborate on cost-efficient data architectures (tiering, caching, Parquet/Delta formats)

• Experimentation, Product Integration & Stakeholder Enablement

• Design experiments (A/B, counterfactual evaluation); define guardrails and success criteria with product teams.

• Integrate models via APIs/SDKs with business rules and fallbacks for graceful degradation.

• Produce clear documentation (model cards, decision logs) and present trade-offs to stakeholders.

Qualifications & Skills

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.

• Proven experience in designing, training, and deploying machine learning models and AI solutions.

• Strong programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).

• Hands-on experience with MLOps tools and practices (Docker, Kubernetes, MLflow, CI/CD pipelines).

• Proficiency in data processing and ETL tools (Spark, Databricks) and working with large datasets.

• Knowledge of model optimization techniques (quantization, distillation) and performance tuning for production environments.

• Familiarity with cloud platforms (Azure, AWS, or GCP) and scalable architecture design.

• Understanding of data governance, privacy standards, and compliance requirements.

• Strong analytical and problem-solving skills with attention to detail.

• Excellent communication skills to collaborate with cross-functional teams and present technical concepts clearly.

… more