Senior Machine Learning Engineer
About this position
The Senior Machine Learning Engineer will act as an expert in machine learning and statistics, developing high-performance systems for various applications, and collaborating on system design and implementation.
Responsibilities
• Act as an expert in the area of machine learning/statistics.
• Develop high-performance machine learning systems for detecting abnormality, intrusion, fraud, masquerading.
• Research and implement machine learning and statistical data mining techniques.
• Collaboratively architect the system design.
• Implement machine learning solutions for software security problems on a variety of platforms.
• Research and prototype algorithms.
• Design and build real-time systems.
• As assigned, assumes complete or partial responsibility for one or more tasks that make up the complete project.
Requirements
• Bachelor’s Degree or Master’s Degree in e.g. computer engineering, computer science, Machine Learning, Artificial Intelligence, Apply Mathematic or related.
• Proficiency with a deep learning framework such as TensorFlow or PyTorch.
• Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas.
• Proficiency in Machine Learning Algorithms in a broad range including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Expertise in visualizing and manipulating big datasets.
• Ability to select hardware to run an ML model with the required latency.
• Knowledge in software development life cycle (Preferably experience of traditional methods as well as agile processes).
• At least 2 years software development experience.
• Experienced in Machine Learning Model Development: Design, develop, and implement advanced machine learning models and algorithms tailored to behavioral analytics, network graph analysis, and peer group analysis.
• Behavioral Analytics: Analyze user behavior patterns and trends to create predictive models that drive customer engagement, retention, and personalization strategies.
• Network Graph Analysis: Develop and apply network graph algorithms to understand and visualize complex relationships within large datasets, enhancing insights into social, organizational, or transaction networks.
• Peer Group Analysis: Utilize clustering and comparative analysis techniques to identify and analyze peer groups, driving targeted strategies and optimizing marketing and product development.
• Experienced in MapReduce / cluster computing frameworks and libraries, such as Spark, Hadoop.
• Experienced in MLOps: Oversee the deployment, monitoring, and management of machine learning models in production environments. Implement MLOps best practices to ensure scalable, reliable, and efficient machine learning operations.
• Pipeline Development: Design and implement end-to-end ML pipelines, including data preprocessing, model training, validation, and deployment.
• Model Monitoring and Maintenance: Set up systems for monitoring model performance, managing model drift, and updating models as needed to ensure continued accuracy and relevance.
• Automation: Automate routine ML tasks and workflows to enhance efficiency and reduce manual intervention.
• Collaboration: Work with DevOps and engineering teams to integrate ML models into production systems and ensure smooth deployment processes.
• Have an analytical mindset and be able to properly analyze data.