We’re looking for an experienced MLOps / AIOps Engineer with 7–14 years of experience to join our Client’s team, which is developing cutting-edge Generative AI and Large Language Model solutions for the insurance industry.
In this role, you’ll be responsible for designing, maintaining, and scaling the infrastructure that supports Machine Learning and AI projects — primarily in Azure — using Infrastructure as Code and modern DevOps practices.
You’ll work in an international environment, get access to technologies like Azure AI, Azure ML, and the OpenAI API, and have a real impact on building global AI projects from the ground up. We’re looking for someone who combines strong ML, MLOps, and automation skills and wants to help create practical, large-scale AI solutions.
responsibilities :
Design, implement, and maintain scalable MLOps pipelines for model training, validation, deployment, and monitoring
Build and manage cloud infrastructure using Infrastructure as Code tools (Terraform, Pulumi) with a focus on Azure services
Implement horizontal and vertical scaling solutions using Azure Kubernetes Service (AKS), Container Registry, and App Services
Design, setup, execute, and debug CI/CD pipelines for ML model deployment and infrastructure provisioning
Collaborate with data scientists and ML engineers to operationalize machine learning models at scale
Monitor model performance, data drift, and system health using observability tools and implement automated remediation
Optimize infrastructure costs while maintaining high availability and performance standards
Establish and enforce best practices for ML model versioning, experimentation tracking, and reproducibility
Implement security best practices and compliance requirements across AI/ML infrastructure
Provide technical guidance and mentorship to junior team members
Stay current with emerging MLOps tools, platforms, and industry best practices
requirements-expected :
7–14 years of experience in DevOps, Platform Engineering, or Cloud Infrastructure roles
Strong expertise with Microsoft Azure, including Azure Machine Learning, AKS, Azure DevOps, and serverless services
Advanced proficiency in Infrastructure as Code with Terraform and Pulumi
Proven experience designing and scaling containerized, microservices-based architectures
Deep understanding of CI/CD pipelines, automation, and quality gates
Strong scripting skills in Python, Bash, and PowerShell
Hands-on experience with Docker, Kubernetes, and Helm
Knowledge of monitoring and observability tools such as Prometheus, Grafana, ELK Stack, and Azure Monitor
offered :
The opportunity to work on interesting and innovative projects for a client in the insurance industry
A chance to grow under the guidance of experienced AI/ML experts
A training budget to support your technical and professional development
Private medical care and co-financing of a sports card
Flexible working hours and the option to work fully remotely
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of foreign language classes
sharing the costs of professional training & courses
