Machine Learning Engineer

2 days ago

Dubai, Dubai, United Arab Emirates dubizzle Full time
Roles and responsibilities

As the Machine Learning Engineer, you will be participating in exciting projects covering the end-to-end Data Science lifecycle - from raw data cleaning and exploration with primary and third-party systems, through advanced state-of-the-art data visualization and Machine learning development. You will work in a modern cloud-based data warehousing environment hosting Machine Learning models alongside a team of diverse, intense and interesting co-workers. You will liaise with other departments - such as product & tech, the core business verticals, trust & safety, finance and others - to enable them to be successful.

In this role, you will:

  • Work on regression and classification problems on tabular, textual and image data
  • Work on forecasting, anomaly detection and time-series analysis
  • Build recommendation engines
  • Work on GPT-based applications using stock models for various business use cases
  • Query large datasets in AWS Redshift to extract the necessary data that will feed ML models
  • Perform data exploration to find patterns in the data and understand the state and quality of the data available
  • Utilize Python code for analyzing data and building statistical models to solve specific business problems
  • Evaluate ML models and fine tune model parameters considering the business problem behind
  • Collaborate with senior peers to Deploy ML models into production that work as standalone data services
  • Build customer-facing reporting tools to provide insights and metrics which track system performance
  • Participate in the off-hours on call stability rota to support live ML models
  • Own at least one ML product that is in production

Requirements

  • Master's degree in AI, Statistics, Math, Operations Research, Engineering, Computer Science, or a related quantitative field
  • 2+ years of working experience in Machine Learning
  • Experience with AWS is a plus
  • Knowledge in Statistical modelling and maths
  • Intermediate knowledge of Python's ML stack: Pandas, Matplotlib, Sklearn, Tensorflow
  • Intermediate knowledge of machine learning algorithms such as Linear regression, Gradient boosted trees, Neural networks
  • Basic knowledge of SQL, and visualization tools such as Periscope with experience in handling large datasets
  • Basic knowledge of statistical analysis and A/B testing
  • Excellent verbal and written communication
  • Strong problem solving skills
  • Analytical thinking; Conceptual thinking Detail-oriented; Business Acumen
  • Entrepreneurial spirit and ability to think creatively; highly-driven and self-motivated; strong curiosity and strive for continuous learning
Desired candidate profile

. Developing and Implementing Machine Learning Models

  • Model Development: Design and implement machine learning models to address specific business problems (e.g., classification, regression, clustering).
  • Algorithm Selection: Choose appropriate algorithms based on the problem requirements, such as supervised, unsupervised, or reinforcement learning.
  • Model Training and Evaluation: Train models on large datasets and evaluate their performance using metrics like accuracy, precision, recall, F1-score, or AUC (depending on the task).
  • Model Optimization: Tune hyperparameters, adjust algorithms, and experiment with different architectures to improve model performance.

2. Data Processing and Feature Engineering

  • Data Cleaning: Preprocess raw data, handle missing values, outliers, and ensure that the data is clean and suitable for machine learning.
  • Feature Engineering: Extract relevant features from raw data, transforming it into a format suitable for modeling (e.g., scaling, encoding, dimensionality reduction).
  • Data Integration: Combine data from different sources and ensure proper data flow into the machine learning pipeline.

3. Model Deployment and Integration

  • Deploying Models: Work with software engineers to deploy machine learning models into production environments.
  • API Development: Develop APIs for integrating machine learning models into larger systems or applications for real-time or batch inference.
  • Scalability: Ensure that machine learning models can handle large-scale data and can be efficiently used in production environments (e.g., using distributed computing, cloud services like AWS, GCP, or Azure).
  • Monitoring and Maintenance: Continuously monitor model performance in production, detecting issues like data drift or model degradation, and retrain models when necessary.

4. Collaboration with Cross-Functional Teams

  • Working with Data Scientists: Collaborate with data scientists to understand the problem, select the right algorithms, and develop experimental models.
  • Working with Software Engineers: Work with software engineers to integrate machine learning models into software products or services.
  • Business Stakeholders: Translate business requirements into technical solutions and communicate findings to non-technical stakeholders.

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