Machine Learning Engineering Specialist

As a Machine Learning Engineering Specialist at EBANX, you'll be responsible for designing and deploying scalable systems for machine learning models. Your duties include developing and maintaining ML pipelines, implementing CI/CD processes for model deployment, and collaborating with various teams to ensure the smooth integration of models into production. Troubleshooting and optimizing existing ML systems will also be key aspects of your role, ensuring their performance and reliability.

To apply for the Machine Learning Engineering Specialist position at EBANX, you should have a strong background in MLOps, DevOps, or similar roles. Essential qualifications include experience with cloud platforms like AWS, GCP, or Azure, proficiency in containerization tools such as Docker, and familiarity with ML frameworks like TensorFlow or PyTorch. Strong Python coding skills and experience with CI/CD tools will greatly enhance your application.

EBANX is committed to the growth and development of all employees, including those in the Machine Learning Engineering Specialist role. We provide opportunities for continuous learning through programs like EBANX Education, which offers financial support for undergraduate, graduate, and MBA studies. Additionally, team members have access to budgets for workshops and courses to further hone their skills and advance their careers.

At EBANX, the work culture is vibrant and inclusive, where creativity and collaboration are highly valued. As a Machine Learning Engineering Specialist, you will join a diverse team of innovators committed to achieving the company's mission of providing global access through payments. We emphasize open communication, personal development, and a balanced work-life culture that includes flexible hours and various wellness programs.

EBANX offers a comprehensive benefits package for Machine Learning Engineering Specialists that includes health and dental insurance, meal allowances, transportation vouchers, and life insurance fully covered by the company. Additional perks include Spanish, English, and Portuguese classes, wellness programs, and a flexible leave policy that supports caregivers. We aim to foster a supportive environment for all our ebankers!

When answering this question, highlight specific projects where you designed MLOps architectures. Discuss the challenges you faced and how you overcame them. Emphasize your experience with cloud platforms and CI/CD tools that you used for implementing and managing ML pipelines.

Be honest about your experience with frameworks like TensorFlow, PyTorch, or Scikit-learn. Discuss any projects where you utilized these frameworks and what you learned during the process. This will demonstrate your practical knowledge and ability to apply frameworks in real-world situations.

Discuss the importance of adhering to security protocols and compliance standards when deploying ML models in financial systems. You can mention specific methodologies or tools you’ve used to ensure data protection and compliance, such as encryption techniques and regular audits.

Explain your approach to troubleshooting, which may involve systematic debugging steps, monitoring key metrics, and collaborating with team members to gather insights. Provide an example of a time you troubleshot a production issue and the solution you implemented.

Talk about techniques you use for optimizing models, such as hyperparameter tuning, feature engineering, and utilizing scalable infrastructure. Provide examples of successful optimizations you've conducted and their impacts on system performance.

Detail your familiarity with CI/CD workflows, including the tools and processes you’ve used for automating model deployments. Illustrate how CI/CD has improved the efficiency of model management in your previous work.

Emphasize the importance of teamwork and effective communication when working with data scientists, software engineers, and other teams. Share examples of how collaboration led to innovative solutions in previous projects.

Discuss various resources you utilize to stay informed, such as online courses, webinars, research papers, or professional communities. This shows your commitment to continuous learning and adapting to industry advancements.

Be honest about the challenges you've encountered, such as integrating models into production or ensuring compliance. Discuss the strategies you implemented to overcome these hurdles, reflecting your problem-solving skills.

Share your philosophy on mentorship, highlighting your experiences of guiding team members in MLOps methodologies. Discuss specific activities you've conducted to promote knowledge sharing and skill development within your team.