Machine Learning Engineer

Responsibilities:

• Model Development: Develop and implement machine learning models to solve business problems.
• Influence: Advocate for the use of machine learning at all levels. Align machine learning initiatives with the overall business strategy.
• Innovation: Drive innovation and automation across the machine learning teams with metrics for benefit realization and sustainability.
• Data Preparation: Responsible for data collection, cleaning, and preprocessing to ensure high-quality data for model training.
• Model Training and Evaluation: Train and evaluate machine learning models, ensuring they meet performance standards.
• Deployment and Monitoring: Deploy machine learning models into production and monitor their performance, adjusting as necessary.
• Collaboration: Work with data scientists, software engineers, and other stakeholders to design scalable, reliable machine learning systems.
• Monitoring and Automation: You will be responsible for monitoring software systems and automating tasks.
• You will automate repetitive tasks to reduce manual intervention and create dashboards for monitoring and management.
• Incident Prevention: You will be expected to prevent incidents or drive down a number of incidents within a period.
• You will be expected to design databases for scalability.
• You will be expected to design databases with the best practices to support varying application complexities.
• You will be expected to optimize databases, queries, etc, while working with diverse teams.
• Security and Compliance: Ensure machine learning systems are secure, apply security best practices, and monitor for vulnerabilities.

Requirements:

• University degree in computer science or “STEM” major (Science, Technology, Engineering, and Math) or related field.
• Minimum of 2-3 years of experience in machine learning and data science.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Proficiency in programming languages such as Python, R, or Java.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with data processing tools like Hadoop, Spark, or Kafka.
• Experience with version control systems like Git.
• Knowledge of software development practices and agile methodologies.
• Experience with automation tools Ansible, Puppet, CHEF, Terraform, etc
• Experience with ELK, Prometheus, Grafana, Kubernetes, CI/CD Platforms, e.g, Jenkins, etc
• Ability to set and manage priorities judiciously.
• Excellent written and oral communication skills.
• Ability to articulate ideas to both technical and non-technical audiences.
• Exceptionally self-motivated and directed.
• Superior analytical, evaluative, and problem-solving abilities.
• Exceptional service orientation.
• Ability to motivate in a team-oriented, collaborative environment.