In this role, you will be instrumental in building a robust and efficient pipeline. You will take on responsibilities such as deploying to the Kubernetes cluster and designing the generation pipeline to ensure seamless model execution.

#LI-DNI

Responsibilities

Design and implement the production generation pipeline
Identify business problems addressable with machine learning
Collect, clean, and prepare data for model training
Prepare containers for Kubernetes deployment
Monitor and troubleshoot the pipeline for any errors or performance issues
Collaborate with other engineers and data scientists to optimize the pipeline and improve model performance
Select suitable machine learning algorithms, build, and train models
Stay updated with the latest advancements in machine learning infrastructure and deployment technologies

Requirements

5+ years of experience in machine learning engineering with a focus on classification metrics for machine learning
Leadership experience in data science projects
Background in Python programming and machine learning libraries like PyTorch and Lightning
Understanding of containerization technologies like Docker and familiarity with Kubernetes
Proficiency in cloud platforms like Amazon Web Services, specifically S3 and EC2 instances
Experience in Databricks
Analytical and problem-solving mindset
Fluency in English at B2 level or higher

Nice to have

Certifications in machine learning or data science
Knowledge of diffusion models architecture
Familiarity with model deployment tools like Weight & Biases and SageMaker Endpoints
Experience with Git and version control systems
Basic understanding of model compression techniques