Embark on an exciting journey as a Data Engineer Advisor, specializing in Machine Learning Engineering. This role offers a unique opportunity to architect and manage the infrastructure crucial for deploying cutting-edge ML models in both real-time and batch processing environments. You'll be instrumental in building robust Python APIs to serve these models and collaborate across teams to seamlessly integrate ML services into user-facing applications.

Join a dynamic team and influence best practices in scalable deployments. This position involves deep collaboration with platform engineers, ensuring our infrastructure aligns with industry standards. You'll also play a key role in maintaining code quality through pull request reviews and contributing to the continuous improvement of our ML engineering practices.

Take ownership of our deployment framework, overseeing the flow of data into our data science lifecycle. This includes driving the automation of the entire data science process, from dataset preparation and model training to evaluation, deployment, and ongoing monitoring in production. Your passion for collaboration and dedication to excellence will be highly valued.

Develop and maintain robust infrastructure for deploying ML models in real-time and batch settings. Build and manage Python APIs using Flask or FastAPI for seamless ML model serving. Collaborate with cross-functional engineering teams to integrate ML services into user-facing applications. Partner with platform engineers to ensure scalable deployments and adherence to infrastructure best practices. Enhance code quality by reviewing pull requests and contributing to the MLE team's standards. Monitor and maintain cloud-based ML services, guaranteeing optimal reliability and performance. Design and implement efficient CI/CD pipelines for effective ML model deployment. Write comprehensive unit tests and apply object-oriented programming principles for maintainable code. Provide support for data modeling and cloud networking tasks as required. Contribute to the enhancement of our model registry, including tracking model deprecation and implementing monitoring strategies. Own the deployment framework for all data science services, from data ingestion to the lifecycle. Drive the automation of the data science lifecycle for production readiness.

A Bachelor's or Master's degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, Physics, or Engineering, or an equivalent qualification, is typically expected.

We are seeking candidates with 8-10 years of overall professional experience, including 3-5 years specifically as an ML Engineer.

A strong grasp of core data science principles and a keen understanding of the challenges involved in migrating research code to production environments are essential.

Demonstrated hands-on experience in machine learning engineering, encompassing the deployment, monitoring, and maintenance of ML models in production, including various architectures like neural networks and random forests.

Solid expertise as a Python developer, particularly within a machine learning engineering context, with proficiency in Flask/FastAPI, OOP, and unit testing.

A strong command of software engineering best practices and experience with Test-Driven Development (TDD).

Proven experience with Infrastructure as Code (IaC) tools such as Terraform.

Extensive hands-on experience with major cloud platforms including GCP, AWS, or Azure.

Familiarity with containerization technologies like Docker and orchestration of deployments is expected.

Experience with CI/CD tools and Git-based development workflows is necessary.

A good understanding of API operations monitoring and logging is required.

Excellent problem-solving abilities and the capacity to work independently on technical challenges.

Familiarity with Agile methodologies and experience contributing to Agile teams is beneficial.