Who we are

We are seeking a highly skilled and motivated Data DevOps Engineer to join our team. The ideal candidate will have a strong background in data engineering, DevOps tasks with Terraform and artificial intelligence, along with proven experience in the Microsoft Azure Cloud environment. This role requires excellent problem-solving, analytical, communication, and collaboration skills.

What you'll be doing

• Oversee the creation, configuration, operation, and troubleshooting of infrastructure components
• Manage the deployment and maintenance of machine learning models
• Perform code refactoring, particularly in Python, to enhance performance and maintainability
• Develop and maintain data pools, including NoSQL databases (MongoDB and CosmosDB) and SQL databases (PostgreSQL)
• Design and implement CI/CD deployment pipelines for ETL processes, machine learning applications, and data pools
• Leverage Azure Cognitive Services and other AI/ML frameworks to develop and deploy AI solutions
• Implement infrastructure deployment using Terraform
   

What you'll bring along

• University Degree in Engineering, Computer Science,  or a related field 
• Minimum of 2-3 years of experience in a similar role
• Proficiency in programming languages such as Python and SQL (Javascript/Typescript is a plus)
• Proven experience with Microsoft Azure Cloud environment and services (e.g., AI Service, Azure Data Factory, Azure Synapse Analytics)
• 3+ years of experience in data engineering, with a focus on building and managing ETL pipelines
• Experience with SQL and NoSQL database
• Familiarity with DevOps practices and CI/CD pipelines
• Knowledge of infrastructure deployment via Terraform
• Familiarity with GenAI models such as LLM and natural language processing techniques
• Experience with Azure Cognitive Services and other AI/ML frameworks
• Knowledge of LLM and RAG solutions is preferred
• Nice to have: understanding of data modeling, data warehousing, and big data technologies
• Nice to have: knowledge of data governance and data security best practices