Machine Learning Engineer

As a Machine Learning Engineer on our Business Insights team, you’ll be responsible for enhancing our internal process and scaling our ML delivery capabilities. As an ML-Ops Engineer, you're expected to scale the solutions our data science team produces, transforming ideas into industry-grade products. You’ll work closely with our Data Scientists to help develop intelligent algorithms capable of learning, analyzing, and predicting future events, turning those algorithms into AI models and systems, and then testing and maintaining them. Your responsibilities will include:

• Driving the operationalization of solutions deployed in production and help the team grow and cultivate best practices in software development and MLOps.

• Architect and lead the development of Machine Learning solutions that can handle low latency, high availability, and high-volume scenarios.

• Mentor engineers and data scientists, providing technical guidance across multiple projects simultaneously while managing competing priorities effectively within agreed-upon timelines.

• Deployment and Integration: Collaborate with software engineers to deploy ML models into production systems and integrate them seamlessly with existing platforms.

• Continuous Improvement: Stay updated with the latest advancements in Machine Learning and apply this knowledge to enhance existing models and develop new ones.

What will help you succeed

• Proficiency in Python and relevant ML/MLOps libraries (MLFlow, FastAPI, Scikit Learn, TensorFlow, PyTorch, etc.).

• Experience with data preprocessing, operations best practices, and model training, validation, evaluation, optimization, and monitoring.

• Two years of experience in AWS (or Azure).

• Solid understanding of CI/CD, DevOps pipelines, and infrastructure as code.

• Enjoy teamwork, problem-solving, and supporting internal customers.

• Master’s degree in Engineering, Computer Science, Mathematics, or another quantitative field.

Preferred Requirements

• Experience with Snowflake and Dataiku.

• Ability to deal with ambiguity, prioritizing needs, and delivering results in a dynamic environment.

• Currently part of a team running a customer-facing ML-based product.

Expectation:

• All Insights team members are expected to travel at least 1 time per year for annual team meetings.

Dynatrace believes that potential is defined by more than qualifications or background. If you're passionate about this job, working in a tech environment, and are eager to learn, we invite you to apply.

Why you will love being a Dynatracer

**This role can be virtual or hybrid depending on the individual and business needs. **