DevSecOps Expert in AI

Offer

• B2B contract
• Remote work with one trip per month to Luxembourg (negotiable)
• Rate: 530–570 EUR/MD

Tasks

• Design and maintain AI infrastructure across cloud, on-premises or hybrid environments for AI development, training and inference, including compute, storage, networking and GPU resources.
• Build and maintain CI/CD and MLOps pipelines for code, data workflows, model training, testing, validation and deployment.
• Use Docker and Kubernetes to package, deploy and scale AI applications and services reliably across environments.
• Enable the transition of machine learning models from development to production, ensuring reproducibility, versioning, traceability and controlled releases.
• Implement monitoring, logging, alerting and performance tracking for infrastructure, applications and AI model endpoints, including availability, latency, resource usage and failures.
• Optimise infrastructure and deployment processes so AI services can scale efficiently and remain stable under changing workloads.
• Apply security best practices for infrastructure, access management, secrets handling, software dependencies, data protection and regulatory compliance.
• Standardise development, test and production environments using Infrastructure as Code and configuration management tools to ensure consistency and reproducibility.
• Collaborate with data scientists, AI engineers, software developers, cybersecurity specialists and business teams to deliver AI solutions.
• Contribute to artefact versioning, auditability, lineage, backup, recovery and lifecycle management for datasets and models.
• Investigate incidents, deployment issues and performance bottlenecks; continuously improve automation, resilience and operational efficiency.

Skills

• Master’s degree in an IT-related field.
• Expertise in DevOps practices and CI/CD pipeline design.
• Very good knowledge of containerisation and orchestration principles.
• Very good knowledge of AI/ML technologies.
• Ability to keep pace with emerging technologies and solutions, especially in generative AI.
• Strong analysis and problem-solving skills.
• Ability to write clear, structured technical documentation.
• Strong communication skills for technical and non-technical audiences.
• Ability to deliver business and technical presentations.
• Ability to participate in and lead technical meetings.

Specific expertise

• Experience with GitLab and CI/CD pipelines.
• Docker and Kubernetes expertise for container orchestration.
• Knowledge of Data Science and Machine Learning principles and AI frameworks such as Haystack and LangChain.
• Experience with AWS or Azure cloud services supporting data-driven insights with DevOps practices.
• Terraform proficiency for Infrastructure as Code.
• Python proficiency; experience with Git, GitLab, Jira and Confluence.
• Knowledge of software development best practices and integration of automated security testing throughout the software development lifecycle.
• Familiarity with CI/CD monitoring and observability, including structured logging and metrics collection using Prometheus and Grafana.
• Familiarity with database management systems such as PostgreSQL and search engines such as Elasticsearch.