Machine Learning Engineer
Full time
at Halian
in
UAE
Posted on January 5, 2025
Job details
Roles and Responsibilities
Machine Learning Model Development- Designing Machine Learning Models
- Developing, testing, and implementing machine learning models, including supervised, unsupervised, and reinforcement learning algorithms.
- Designing models to handle specific tasks such as classification, regression, anomaly detection, and recommendation systems.
- Data Preprocessing and Feature Engineering
- Cleaning, transforming, and normalizing data to make it suitable for machine learning algorithms.
- Performing feature selection and extraction to identify the most relevant data features for model training.
- Algorithm Selection and Tuning
- Selecting the most appropriate algorithms based on the problem, such as deep learning, decision trees, random forests, support vector machines (SVM), or natural language processing (NLP) models.
- Tuning hyperparameters using techniques like grid search, random search, and Bayesian optimization to optimize model performance.
- Model Evaluation and Validation
- Evaluating model performance using metrics such as accuracy, precision, recall, F1 score, ROC curves, and confusion matrices.
- Conducting cross-validation and A/B testing to assess model generalizability and robustness.
- Model Deployment
- Deploying machine learning models into production environments and ensuring they integrate with existing systems and workflows.
- Using tools like TensorFlow Serving , Seldon , Kubeflow , or cloud-based solutions (e.g., AWS SageMaker , Azure ML , Google AI Platform ) to deploy models.
- Scalability and Optimization
- Ensuring that models scale efficiently to handle large volumes of data or real-time data streams.
- Optimizing models for speed, memory usage, and inference time, particularly for production environments where performance is critical.
- Monitoring and Maintenance
- Continuously monitoring the performance of deployed models and updating them as necessary based on new data, changing conditions, or model drift.
- Implementing automatic retraining mechanisms to adapt models to evolving datasets or business requirements.
- Leading Machine Learning Projects
- Leading a team of engineers and data scientists in machine learning projects, providing technical guidance and ensuring timely delivery.
- Collaborating with cross-functional teams, including data engineers, software engineers, product managers, and business stakeholders, to understand requirements and define project goals.
- Mentorship and Knowledge Sharing
- Mentoring junior engineers and data scientists, helping them grow their technical skills and knowledge in machine learning and AI.
- Promoting best practices in code quality, model development, and deployment, and encouraging a culture of continuous learning within the team.
- Stakeholder Communication
- Communicating the results of machine learning projects to both technical and non-technical stakeholders, explaining the implications of models, predictions, and findings.
- Providing recommendations on how machine learning models can drive business value and support decision-making.
- Staying Updated with Latest Advancements
- Keeping up with the latest research in machine learning, AI, and data science to apply new techniques and methodologies to improve models and systems.
- Experimenting with cutting-edge technologies such as deep learning , reinforcement learning , transformers , and generative models .
- Contributing to Research and Development
- Publishing research papers or contributing to open-source machine learning projects to advance the field and build the organization’s reputation in AI.
- Strong Analytical and Problem-Solving Skills
- Ability to approach complex problems and break them down into solvable components using advanced machine learning techniques.
- Expertise in identifying the best model or algorithm for a given problem based on the data and business requirements.
- Proficiency in Programming Languages
- Expertise in programming languages commonly used in machine learning, such as Python , R , Java , or C++ .
- Experience with machine learning libraries and frameworks like TensorFlow , PyTorch , Keras , Scikit-learn , XGBoost , and LightGBM .
- Deep Understanding of Machine Learning Algorithms
- In-depth knowledge of various machine learning algorithms (e.g., neural networks, decision trees, clustering, deep learning, natural language processing) and when to apply them.
- Familiarity with specialized models for different tasks, such as Convolutional Neural Networks (CNNs) for image processing or Recurrent Neural Networks (RNNs) for sequential data.
- Mathematics and Statistics
- Strong background in mathematics, particularly in statistics, probability, linear algebra, and calculus, as these are foundational to machine learning algorithms.
- Ability to understand and apply mathematical concepts such as optimization, loss functions, and gradient descent.
- Data Engineering and Data Manipulation
- Experience with data wrangling, working with large datasets, and using tools like Pandas , NumPy , Dask , or Spark for data manipulation.
- Ability to create data pipelines and preprocess data effectively for machine learning applications.
Desired Candidate Profile
As a Senior MLOps Engineer, you will build and manage the MLOps infrastructure, working closely with Data Scientists and Engineers to automate machine learning workflows, manage deployments, and optimize CI/CD pipelines. This role involves setting up scalable environments and ensuring robust versioning and deployment of ML models. Key Responsibilities:- Develop and manage CI/CD pipelines for ML workflows.
- Automate ML model deployment across production, staging, and testing environments.
- Collaborate with cross-functional teams to enhance model training, validation, and deployment.
- Monitor and optimize MLOps pipelines for performance and reliability.
- Implement and document MLOps infrastructure and best practices.
- 7+ years in CI/CD pipeline management, preferably in ML.
- Strong experience with Docker, Kubernetes, and machine learning frameworks (e.g., TensorFlow, PyTorch).
- Proficiency in scripting languages (Python, Bash).
- Familiarity with data storage engines (NoSQL, SQL, Elasticsearch).
- Knowledge of distributed systems and web software development.
- Strong communication and collaboration skills.
Apply safely
To stay safe in your job search, information on common scams and to get free expert advice, we recommend that you visit SAFERjobs, a non-profit, joint industry and law enforcement organization working to combat job scams.
