[Customer Platforms] Sênior Machine Learning Engineer

As a Sênior Machine Learning Engineer at Stone Tech, you will be responsible for developing and implementing innovative machine learning models, especially for conversational products. Your role includes collaborating with diverse teams to design and enhance ML pipelines, utilizing tools such as Databricks and Sagemaker. Additionally, you'll ensure best engineering practices are followed, participate in rapid experimentation, and help in the deployment of these machine learning solutions.

To qualify for the Sênior Machine Learning Engineer position at Stone Tech, you should have extensive experience in Python, particularly in developing machine learning solutions. Familiarity with MLOps tools like Databricks and Snowflake, alongside knowledge of cloud platforms such as AWS, GCP, or Azure, is essential. Experience with deploying models using CI/CD practices and creating APIs for database communication is also vital for this role.

To enhance your candidacy for the Sênior Machine Learning Engineer role at Stone Tech, experience in projects involving Large Language Models (LLMs) would be beneficial. Additionally, familiarity with natural language processing (NLP) techniques and information retrieval will set you apart. Understanding optimization techniques will also elevate your profile for this exciting position.

At Stone Tech, the work culture emphasizes autonomy, teamwork, and continuous improvement, which greatly supports Sênior Machine Learning Engineers. The company values proactive solutions and flexible working arrangements, enabling you to explore innovative ideas while collaborating with a diverse and talented team on meaningful projects that impact customer experience.

Sênior Machine Learning Engineers at Stone Tech enjoy a comprehensive benefits package that includes health and dental plans, digital health services, meal or food vouchers, and remote work support. You'll also benefit from educational resources for continuous development, flexible working hours, and wellness programs, creating a balanced and fulfilling work environment.

Begin by highlighting specific projects where you've used Python for ML solutions. Discuss libraries and frameworks you have utilized, such as TensorFlow, scikit-learn, or PyTorch, and illustrate how these contributed to successful outcomes in your projects.

Reference a few machine learning models you have worked with, explaining the problem they addressed, the data used, and the results achieved. Be prepared to discuss the decision-making process behind selecting specific models.

Discuss your experience with deployment pipelines using CI/CD practices, mentioning specific tools like Jenkins or GitHub Actions, and platforms like AWS Sagemaker or Azure ML. Illustrate your process for ensuring smooth and efficient deployment of models.

Choose a relevant challenge, ideally one that illustrates your problem-solving skills. Describe the context, what strategies you employed to tackle the issue, and the outcome. Emphasize your analytical approach and what you learned from the experience.

Share your understanding of MLOps principles, focusing on version control, monitoring, and model management. Describe instances where you applied these practices in your projects to enhance collaboration and streamline ML workflows.

Provide examples of your experience working alongside diverse teams, including product managers, data scientists, and business analysts. Highlight your communication skills and how they contributed to achieving common goals.

Discuss important metrics such as accuracy, precision, recall, and F1 score. Explain how you choose metrics based on the project requirements and how you use them to refine and improve your models.

Talk about specific NLP projects you have worked on, detailing the techniques applied, such as tokenization, semantic analysis, or text classification. Mention any tools or libraries you've utilized, such as NLTK or SpaCy.

Share your approach for staying informed, whether through industry blogs, academic journals, webinars, or professional networks. Emphasize your commitment to lifelong learning and integrating new knowledge into your work.

Express your admiration for Stone Tech’s impact on the payment industry and its commitment to innovation. Align your personal values and career aspirations with the company's mission and culture, demonstrating your enthusiasm for contributing to their success.