Machine Learning Engineer / Data Scientist

Eitacies - Chicago, IL

Full-time - Mid Level

Remote - Chicago, IL

Professional, Scientific, and Technical Services

About the position

The Machine Learning Engineer Lead Level 3 at EITACIES is responsible for designing and implementing machine learning models and infrastructure solutions that enhance data handling and model deployment. This role requires a strong understanding of machine learning algorithms, particularly in anomaly detection, and the ability to integrate these models into existing systems. The position emphasizes collaboration with IT teams and adherence to architectural standards to ensure high performance and reliability of solutions.

Responsibilities

• Ensure adherence to architecture standards and roadmaps.
• Implement overall infrastructure/middleware components per project with infrastructure teams, ensuring that overall solution meets SLA (performance and up-time), DR, and scalability needs.
• Design and successfully implement overall infrastructure/middleware components per project with infrastructure teams.
• Ensure that post-production operational processes/deliverables are well designed and implemented prior to the project moving into the solution support phase.
• Define and create operational procedures, processes, and scripts.
• Follow appropriate change/release management practices.
• Support and maintain infrastructure solutions utilizing required tools and technologies, providing off-hours support (24 x 7) as required.

Requirements

• Strong understanding of machine learning algorithms and principles, especially those relevant to anomaly detection, such as supervised and unsupervised learning, clustering, and neural networks.
• Proficiency in Python programming and data handling.
• Experience integrating ML models into existing systems, including Azure big data and analytics.

Nice-to-haves

• Analytical thinking and problem-solving skills.
• Creative problem-solving capacity to devise innovative solutions for anomaly detection and prediction.
• Attention to detail in model tuning and data interpretation.
• Clear communication skills to explain complex ML concepts to non-technical stakeholders.
• Collaborative spirit to work closely with IT teams and other departments.