Overview

Warehousing and logistics systems play an increasingly critical role in contributing to the competitiveness of many companies and, at the same time, to the effectiveness of the general world economy.

Responsibilities

• Develop end-to-end ML pipelines encompassing the ML lifecycle from data ingestion, data transformation, model training, model validation, model serving, and model evaluation over time.
• Proficiency in PyTorch, Ray Serve, and deep learning concepts, with hands-on experience in developing, deploying, scaling, and optimizing deep learning models for production environments.
• Collaborate closely with AI scientists to accelerate productionization of ML algorithms.
• Setup CI/CD/CT pipelines for ML algorithms.
• Deploy models as a service both on-cloud and on-prem.
• Learn and apply new tools, technologies, and industry best practices.
• MS in Computer Science, Software Engineering, or equivalent field
• Experience with Cloud Platforms, especially GCP and Azure, and related skills: Docker, Kubernetes, edge computing
• Familiarity with task orchestration tools such as MLflow, Kubeflow, Airflow, Vertex AI, Azure ML, etc.
• Fluency in at least one general purpose programming language. Python or Java preferred. Strong DevOps skills: Linux/Unix environment, testing, troubleshooting, automation, Git, , dependency management, and build tools (GCP Cloud Build, Jenkins, Gitlab CI/CD, Github Actions, etc.).
• Data engineering skills a plus, such as Beam, Spark, Pandas, SQL, Kafka, GCP Dataflow etc.