Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges
Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production
Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents
Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models
Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems
Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation
ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow
Developing infrastructure components and services using cluster solutions like Kubernetes
The development of Python-based backend services
Creating and maintaining ML pipelines, including legacy ones
Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar