Machine Learning Engineer
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Key skills for this role
About the Role
PWN-ALL is seeking an experienced Machine Learning Engineer to design, develop, deploy, and maintain production-grade machine learning solutions.
Key Skills for This Role
Responsibilities
- Design, develop, train, validate, and optimize machine learning models
- Build scalable and reliable machine learning pipelines
- Prepare, clean, transform, and analyze structured and unstructured datasets
- Perform feature engineering, model selection, hyperparameter tuning, and error analysis
- Deploy machine learning models and AI services into production environments
- Develop and maintain APIs and backend services for model inference
- Monitor model performance, latency, data quality, and model drift
- Establish reproducible experimentation, model versioning, and documentation practices
- Collaborate with engineering and product teams to translate business requirements into technical solutions
- Conduct code reviews and contribute to engineering standards and best practices
Requirements
- At least 3 years of commercial experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or comparable role
- Strong proficiency in Python
- Production experience with one or more ML frameworks: PyTorch, TensorFlow, Scikit learn, XGBoost, or LightGBM
- Strong understanding of supervised and unsupervised learning
- Experience with data preprocessing, feature engineering, model evaluation, and hyperparameter optimization
- Strong knowledge of SQL and experience with relational or analytical databases
- Experience deploying machine learning models into production
- Experience designing or integrating REST APIs
- Professional working proficiency in English and Russian
Full Job Posting
About the Role
- We are seeking an experienced Machine Learning Engineer to design, develop, deploy, and maintain production grade machine learning solutions.
- You will be responsible for the complete machine learning lifecycle, from data preparation and experimentation to production deployment, monitoring, and continuous improvement.
- Professional working proficiency in both English and Russian is mandatory due to regular communication with English and Russian speaking team members and stakeholders.
Key Responsibilities
- Design, develop, train, validate, and optimize machine learning models.
- Build scalable and reliable machine learning pipelines.
- Prepare, clean, transform, and analyze structured and unstructured datasets.
- Perform feature engineering, model selection, hyperparameter tuning, and error analysis.
- Define appropriate model evaluation metrics and validation strategies.
- Deploy machine learning models and AI services into production environments.
- Develop and maintain APIs and backend services for model inference.
- Monitor model performance, latency, data quality, and model drift.
- Improve the reliability, scalability, and cost efficiency of machine learning systems.
- Establish reproducible experimentation, model versioning, and documentation practices.
- Collaborate with engineering and product teams to translate business requirements into technical solutions.
- Conduct code reviews and contribute to engineering standards and best practices.
Required Qualifications
- At least 3 years of commercial experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or in a comparable technical role.
- Strong proficiency in Python.
- Production experience with one or more machine learning frameworks, including PyTorch, TensorFlow, Scikit learn, XGBoost, or LightGBM.
- Strong understanding of supervised and unsupervised learning.
- Practical knowledge of classification, regression, clustering, ranking, forecasting, or recommendation systems.
- Experience with data preprocessing, feature engineering, model evaluation, and hyperparameter optimization.
- Strong knowledge of SQL and experience working with relational or analytical databases.
- Experience deploying machine learning models into production.
- Experience designing or integrating REST APIs.
- Working knowledge of Git, automated testing, code review, and software development best practices.
- Understanding of statistics, probability, linear algebra, and machine learning fundamentals.
- Ability to write maintainable, tested, and well documented production code.
Preferred Qualifications
- Experience with AWS, Microsoft Azure, or Google Cloud Platform.
- Experience with Docker and containerized model deployment.
- Knowledge of Kubernetes and cloud native infrastructure.
- Experience with MLOps tools such as MLflow, Kubeflow, Airflow, Weights & Biases, or similar platforms.
- Experience building CI/CD pipelines for machine learning systems.
- Knowledge of model monitoring, observability, data validation, and drift detection.
- Experience with distributed data processing technologies such as Spark.
- Experience with natural language processing, large language models, computer vision, recommendation systems, or predictive analytics.
- Familiarity with retrieval augmented generation, vector databases, embeddings, or LLM evaluation.
- Understanding of data privacy, information security, responsible AI, and model governance.
- Experience working in an international or multilingual environment.
Compensation and Benefits
- AED 37,000 gross monthly base salary + bonuses
- AED 444,000 annualized gross base salary.
- AED 100 meal allowance for each working day, paid in addition to the base salary.
- Full time employment.
- Four day working week, Monday through Thursday.
- Working hours from 10:30 AM to 6:00 PM, Dubai time.
- Hybrid working arrangement.
- Modern and collaborative working environment.
- Opportunity to work on production grade machine learning and AI systems.
- Direct involvement in technical and product decisions.
- Professional development and career growth opportunities.
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