AI ML Engineer
At a glance
Mid-level Machine Learning Engineering role at Apparel Career Site. United Arab Emirates · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Turn business needs into ML models, choose architectures, and set measures for success
- Build repeatable model workflows from feature creation through training, tuning, and packaging
- Improve CPU and GPU inference speed and throughput, then assess fairness, calibration, and other measures beyond accuracy
- Deploy batch and real-time model services, manage releases, and watch for drift, regressions, and cost changes
- Create tested data pipelines and quality controls, integrate models with product APIs, and document decisions for stakeholders
What you bring
- A bachelor's or master's degree in computer science, data science, AI/ML, or a related area
- Practical experience designing, training, and putting machine learning models and AI solutions into production
- Strong Python skills, familiarity with TensorFlow, PyTorch, or Scikit-learn, and experience with MLOps tools such as Docker or Kubernetes
- Experience processing large datasets with Spark, Databricks, or another listed tool, plus knowledge of production model optimization
- Understanding of cloud platforms, scalable system design, and data privacy and governance requirements
Who this fits
This role suits an engineer who can carry models from problem definition through production deployment and ongoing monitoring. You'll work with product teams and other stakeholders to set experiment goals, connect models to business processes, and explain technical trade-offs.
From the employer
Key responsibilities
1) 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).
2) 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
3) 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)
4) 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.