Machine Learning Engineer

GeorgiaTEK Systems Inc.; Brasil; On-site; BRL 180,000 - 320,000; Full time

Job summary

GeorgiaTEK Systems Inc. is seeking an experienced ML Engineer to design scalable ML systems and lead end-to-end lifecycle activities from model development to production deployment.

You will guide engineering teams, collaborate with data scientists, data engineers, software engineers, and product managers to deliver robust ML-enabled business applications. The role emphasizes performance optimization, code quality, and deployment best practices, with a focus on continuous improvement and

Qualifications

• 5+ years of professional experience in ML engineering, data science, software engineering, or related fields.
• Proven experience developing and deploying ML models from concept to production.
• Strong understanding of ML system architecture, scalability, reliability, and performance optimization.
• Proficiency in application development, programming, software testing, debugging, and code review.
• Experience with cloud computing and cloud-based ML solutions.
• Knowledge of big data processing, data integration, ETL pipelines, data modeling, and distributed computing.
• Strong mathematical and statistical foundations with advanced ML concepts.
• Demonstrated technical leadership, project ownership, mentoring, or team leadership experience.
• Strong communication and collaboration skills with stakeholders.

Responsibilities

• Design and develop scalable, reliable ML systems and architectures.
• Manage end-to-end ML lifecycle from design to deployment and production optimization.
• Lead technical projects, define scope, and guide engineering teams.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, PMs, and Architects to integrate ML.
• Optimize model performance, system reliability, code quality, and efficiency.
• Participate in architecture discussions, code reviews, testing, and production implementation.
• Establish best practices for software development, ML engineering, and deployment processes.
• Mentor engineers, share knowledge, and promote continuous improvement.
• Research emerging AI/ML techniques and identify opportunities for innovation.

Skills

ML Engineering

Production ML

Cloud Computing

Data Pipelines

Software Development

Team Leadership

Education

Master's degree

Tools

AWS

Azure

GCP

Job description

• Design and develop scalable, reliable, and maintainable machine learning systems and architectures.
• Manage the end-to-end ML lifecycle, from solution design and model development to deployment and production optimization.
• Lead technical projects, define project scope, and guide engineering teams in delivering solutions.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, Product Managers, and Architects to integrate ML solutions into business applications.
• Optimize model performance, system reliability, code quality, and computational efficiency.
• Participate in architecture discussions, code reviews, testing, debugging, and production implementation.
• Establish best practices for software development, ML engineering, and deployment processes.
• Mentor engineers, share technical knowledge, and promote continuous improvement.
• Research emerging AI/ML techniques and identify opportunities for innovation.

Required Qualifications and Skills

• 5+ years of relevant professional experience in Machine Learning Engineering, Data Science, Software Engineering, DevOps, or related fields.
• Proven experience developing and deploying machine learning models from concept to production.
• Strong understanding of ML system architecture, scalability, reliability, and performance optimization.
• Proficiency in application development, programming, software testing, debugging, and code review.
• Experience with cloud computing and cloud-based ML solutions.
• Knowledge of big data processing, data integration, ETL pipelines, data modeling, and distributed computing.
• Strong mathematical and statistical foundations, with the ability to understand and apply advanced ML concepts.
• Demonstrated technical leadership, project ownership, mentoring, or team leadership experience.
• Strong communication and collaboration skills, including the ability to work with technical and business stakeholders.

Preferred Qualifications

• Master's degree in Machine Learning, Artificial Intelligence, Computer Science, or a related field.
• Cloud certifications in platforms such as AWS, Microsoft Azure, or Google Cloud.
• Experience defining technical roadmaps, architecture standards, and engineering best practices.
• Contributions to technical research, white papers, patents, or innovation initiatives.