AWS DevOps Engineer

Job Description

Tieto Tech Consulting (MentorMate from 2001 to 2026) provides design-led, data-centric, and AI-powered digital engineering & consulting services to enterprises worldwide. With a strong heritage of Nordic quality, local presence, and global-scale operations, we are backed by 8,500+ IT professionals. Our clients trust us for advanced data and analytics skills, top industry-specific expertise, specialized software R&D, and the capabilities of a multinational team. We are part of Tieto, a leading technology company with an annual revenue of approximately EUR 2bn.

Tieto Tech Consulting’s team in Bulgaria consists of 500+ professionals specializing in the development of tailored digital solutions in diverse industries. 

We’re looking for a Senior AWS DevOps Engineer to join a global team of our clients, modernizing a cloud-native healthcare SaaS platform used for data-driven and AI-powered solutions. You’ll help shape the infrastructure that enables engineering and data science teams to move faster, deploy safely, and scale confidently. 

This is a hands-on role for someone who enjoys building reliable systems, improving developer experience, automating everything possible, and working close to both platform and AI/ML teams. 

Key Responsibilities 

• Hands-on work with modern AWS services (Fargate, EKS, Lambda, S3, RDS) and CI/CD tooling such as ArgoCD and GitHub Actions to support reliable, scalable, and production-grade systems 
• Lead operations for multi-tenant SaaS workloads on AWS, ensuring scalability, high availability, and cost efficiency 
• Build and maintain scalable infrastructure supporting production applications, data platforms, and AI/ML workloads 
• Improve operational excellence through incident response, postmortems, automation, and proactive monitoring  
• Manage and enhance CI/CD pipelines supporting both application and ML deployment workflows 
• Build and maintain infrastructure automation using Infrastructure as Code (AWS CDK or Terraform) 
• Enable self-service capabilities for engineering and data science teams 
• Monitor and optimize cloud usage across compute, GPU, and storage resources, implementing cost controls and forecasting 
• Support and automate ML pipelines, including training, testing, and deployment using AWS SageMaker, Kubeflow, or MLflow 
• Manage GPU and compute clusters (EKS, ECS, EC2) for model training and inference workloads 
• Develop and maintain monitoring, alerting, observability, and security best practices 
• Collaborate closely with Engineering, Data, AI/ML, and PlatformOps teams to ensure smooth cross-team delivery