We are seeking a highly analytical and experienced Senior Machine Learning Engineer to lead the design, development, and deployment of large-scale machine learning models. In this role, you will move beyond experimental notebooks to build robust, production-grade AI systems that solve complex business problems. You will be responsible for the entire ML lifecycle—from data pipeline architecture to model monitoring and optimization—ensuring that our AI solutions are scalable, ethical, and highly performant.

Key Responsibilities

• Model Architecture: Design and implement state-of-the-art ML models (Computer Vision, NLP, or Predictive Analytics) using frameworks like PyTorch or TensorFlow.

• End-to-End MLOps: Build and maintain automated pipelines for data ingestion, feature engineering, model training, and CI/CD deployment.

• Scalability & Optimization: Optimize models for high-throughput, low-latency production environments, utilizing techniques like quantization and pruning.

• Infrastructure Management: Architect scalable backend infrastructure using Kubernetes, Docker, and cloud-native AI services (AWS SageMaker, Google Vertex AI, or Azure ML).

• System Design: Collaborate with Data Engineers to design robust data schemas and feature stores that support real-time inference.

• Mentorship: Lead code reviews and mentor junior engineers on best practices in software engineering and statistical modeling.

Requirements

• Education: Master or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field.

• Experience: 5+ years of professional experience in Machine Learning, with a proven track record of deploying models into production at scale.

• Programming: Expert-level proficiency in Python (C++ or Go is a plus) and SQL.

• Mathematical Foundation: Deep understanding of linear algebra, probability, statistics, and optimization algorithms.

Technical Stack

• Frameworks: PyTorch, TensorFlow, Keras, or JAX.

• Libraries: Scikit-learn, Pandas, NumPy, XGBoost, and Hugging Face.

• Data Engineering: Spark, Kafka, Airflow, and Snowflake/BigQuery.

• DevOps/MLOps: Docker, Kubernetes, MLflow, Kubeflow, or DVC.

• Cloud: AWS, GCP, or Azure.

Work Details

• Location: [City/Remote Option] (Many roles in the UAE are now Hybrid).

• Schedule: Full-time, Monday – Friday.

• Compensation: Highly competitive Tax-Free Salary + Equity/Stock Options + Performance Bonuses.

• Benefits:

  • Visa: Full UAE Residency sponsorship (Golden Visa eligibility for high-tier tech talent).

  • Health: Premium international medical insurance.

  • Relocation: Comprehensive relocation package (if moving from abroad).

  • Development: Annual budget for conferences, research publications, and specialized certifications.