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We are seeking a highly skilled Machine Learning Engineer with at least 5 years of hands-on experience in designing, developing, deploying, and maintaining machine learning models. The ideal candidate should have a strong foundation in data science, a proven track record of solving real-world problems using ML, and a passion for building scalable and production-grade ML pipelines. Key Responsibilities:
- Design, build, and deploy end-to-end machine learning solutions tailored to business problems.
- Perform data preprocessing, feature engineering, and model selection for various types of structured and unstructured data.
- Develop, train, validate, and fine-tune machine learning models including regression, classification, clustering, recommendation systems, and NLP-based models.
- Build automated model training and evaluation pipelines using industry best practices (e.g., MLFlow, Airflow, Kubeflow).
- Collaborate with data engineers, product teams, and stakeholders to translate requirements into technical implementations.
- Evaluate model performance using appropriate metrics (e.g., precision, recall, AUC, RMSE) and apply techniques like hyperparameter tuning, ensembling, and cross-validation.
- Deploy models to production using containerized environments (e.g., Docker, Kubernetes) and integrate with web services/APIs.
- Continuously monitor model performance and implement model retraining strategies to handle data drift or changing business conditions.
- Document code, workflows, and research findings in a clear, concise, and reproducible manner.
- Stay up to date with the latest ML research and technologies and recommend improvements or new approaches. Required Skills & Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field.
- 5+ years of hands-on experience developing and deploying ML models in production environments.
- Proficient in Python (with libraries like scikit-learn, pandas, NumPy, TensorFlow, PyTorch).
- Solid understanding of statistical modeling supervised and unsupervised learning techniques.
- Experience with data pipelines and orchestration tools such as Airflow, Prefect, or Luigi.
- Hands-on experience with cloud platforms (AWS, GCP, Azure) and MLOps frameworks.
- Knowledge of SQL and experience working with large-scale datasets in relational and NoSQL databases.
- Familiarity with version control systems (e.g., Git) and containerization tools (Docker, Kubernetes). Preferred (Good-to-Have):
- Experience with NLP, Computer Vision, or Time Series Forecasting models.
- Familiarity with distributed computing frameworks such as Spark or Dask.
Key Skills
Ranked by relevance
machine learning
kubernetes
docker
distributed computing
containerization
computer vision
tensorflow
kubeflow
pytorch
python
pandas
mlflow
nosql
cloud
numpy
spark
mlops
git
sql
aws
gcp
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- Posted
- May 13, 2025
- Type
- Full-time
- Level
- Entry
- Location
- Abu Dhabi
- Company
- Halian
Industries
Staffing
Recruiting
Categories
Engineering
Information Technology
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