Description

You will design, implement, and maintain scalable data architectures and ETL processes for large volumes of data in the cloud.

Responsibilities

• Design and maintain scalable data architectures and ETL pipelines in cloud environments.
• Research and implement MLOps tools, frameworks, and platforms to increase organizational maturity.
• Develop and update complex SQL queries and scripts within Oracle Database OS.
• Automate Data Science workflows by introducing agile and automated approaches.
• Conduct internal training and presentations regarding MLOps tools and benefits.

Required Skills

• 5+ years of experience in data engineering with a strong software engineering background.
• Proficiency in Python for both ML model development and automation tasks.
• Experience with MLOps operationalization using frameworks like Kubeflow or AWS SageMaker.
• Expertise in ETL processes, scheduling tools, and database modeling.
• Hands-on experience with AWS, Azure, or GCP.
• Strong knowledge of DevOps, CI/CD/CT, and pipeline implementation.
• Experience with Kubernetes, Bash, and the Unix command line toolkit.
• Solid understanding of ML, AI, LLMOps, and GenAI concepts.
• Experience managing project teams and working with high-dimensional omics data.

Preferred Skills

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
• Experience with R programming and Snowflake or Redshift.

Key Skills

Education

Bachelor’s or Master’s degree