Requirements

• Master’s degree in IT or a relevant discipline, combined with a minimum of 13 years of relevant working experience in IT;
• At least 3 years of experience in LLMs: fine-tuning, prompting strategies, evaluation, and deployment;
• At least 2 years of experience in LangChain or equivalent frameworks for LLM orchestration;
• At least 2 years of experience in Vector databases;
• At least 2 years of experience in Python, with practical use of Jupyter Notebooks;
• At least 2 years of experience in AWS services for AI, data, and cloud-native development;
• At least 1 year of experience in Machine Learning Operations, including CI/CD pipelines and monitoring of AI solutions;
• At least 1 year of experience in data engineering and archiving best practices;
• Good knowledge of Docker, Kubernetes and security best practices in AI and cloud-based deployments would be an advantage;
• Knowledge of DevOps/Infrastructure-as-Code tools beyond AWS (Terraform, Ansible) and data governance frameworks and compliance standards (e.g., GDPR) would be an advantage;
• AWS Solutions Architect, AWS Machine Learning, AWS DevOps Engineer, TensorFlow Developer or equivalent AI/ML credentials, Kubernetes Administrator (CKA), TOGAF or equivalent enterprise architecture framework certifications would be an asset;
• Excellent command of the English language.