Senior Machine Learning Engineer

Riverty

What You'll Be Doing

Implement and Deploy ML Models: Implement, and deploy machine learning models, utilizing a variety of modeling approaches to address different business problems.
Advanced ML Techniques: Implement systems with Generative AI (GenAI) and emerging architectures like Retrieval-Augmented Generation (RAG).
Scalable Training Solutions: Scale training processes across multiple GPUs, leveraging state-of-the-art frameworks such as NVIDIA's cuDNN and TensorRT.
Support AI and ML Pipelines: Assist various teams in deploying AI and ML pipelines on Google Cloud using Vertex AI, ensuring robust and scalable solutions.
Automated ML Pipelines: Design and implement automated machine learning pipelines, including fully automated training and prediction processes.
MLOps Best Practices: Apply MLOps best practices for model deployment, monitoring, and automation, enhancing the efficiency and reliability of ML operations.
Containerization: Work with containerization technologies to streamline the deployment and management of ML models.
Infrastructure-as-Code: Implement infrastructure-as-code using Terraform to ensure reproducible and scalable ML environments.
Innovation and Problem-Solving: Apply strong problem-solving skills and creative thinking to champion the adoption of AI technologies that address business needs.
Collaboration and Communication: Collaborate effectively with cross-functional teams, demonstrating excellent communication and teamwork skills in a fast-paced environment.

What You Bring

Bachelor's or master’s degree in Computer Science, Engineering, Mathematics, or a related field.
Strong proficiency in Python and experience with deep learning frameworks, particularly PyTorch.
In-depth understanding of various modeling approaches, Generative AI, and emerging architectures like Retrieval-Augmented Generation.
Solid understanding of cloud computing concepts and experience with cloud platforms, preferably Google Cloud Platform (GCP).
Familiarity with MLOps best practices and tools for model deployment, monitoring, and automation.
Experience working with containerization technologies and container registries.
Proficiency in designing and implementing automated machine learning pipelines.
Experience with infrastructure-as-code, preferably Terraform.
Strong problem-solving skills and the ability to think creatively to develop innovative solutions.
Excellent communication and teamwork skills, with the ability to collaborate effectively in a fast-paced environment.

It's also a plus if you have...

Hands-on experience with scaling training processes across multiple GPUs, using frameworks like NVIDIA cuDNN and TensorRT.
A understanding of cloud security practices and data compliance protocols.
Experience in leading initiatives for academic collaboration and community engagement.

What We Offer

A modern office in the heart of Berlin and flexible options for remote work.
Numerous benefits such as a free BVG job ticket, a reduced-price membership for Urban Sports, discounts for Bertelsmann employees etc.
Ongoing training opportunities on our internal platforms BMG Campus and Bertelsmann University.
Long-term career opportunities for all employees.
A collaborative work environment with an open, appreciative community of people who all share the same passion for working with artists and songwriters.
Our unique corporate culture based on strong values and mutual support that make us a great team.