📍 Job Overview

Job Title: DevOps Engineer for AI Platform
Company: Teads
Location: Ljubljana, Ljubljana, Slovenia
Job Type: On-site
Category: DevOps Engineer
Date Posted: 2025-07-02
Experience Level: Mid-Senior level (5-10 years)

🚀 Role Summary

Manage and optimize AWS-based solutions, scale ML workloads, and build CI/CD pipelines.
Collaborate with data scientists to support research, training, and deployment of ML models.
Ensure high availability and performance of AI platform infrastructure.

📝 Enhancement Note: This role focuses on managing and optimizing AWS-based solutions for machine learning workloads, requiring strong experience in DevOps principles and AWS management.

💻 Primary Responsibilities

AWS Management: Manage and optimize AWS-based solutions, including AWS Batch, SLURM, EKS, and GPU-optimized workloads.
CI/CD Pipeline Development: Build and maintain CI/CD pipelines using Jenkins and GitLab.
Infrastructure as Code: Utilize Terraform to manage infrastructure as code.
Container Orchestration: Set up and manage Docker containers and orchestrate their deployment in cloud environments.
JupyterHub Management: Manage JupyterHub for seamless collaboration and resource sharing among data scientists.
Data Pipeline Optimization: Monitor and optimize data pipelines, ensuring efficient data flow and model training.
Alerting and Monitoring: Implement automated monitoring and alerting for AI tools usage, business metrics, and pricing.
Cloud Infrastructure Security: Ensure the reliability, scalability, and security of cloud infrastructure.
Performance Optimization: Proactively improve performance, reduce costs, and ensure smooth deployment cycles.

📝 Enhancement Note: This role requires a strong understanding of AWS services, DevOps principles, and machine learning infrastructure to manage and optimize AI platform workflows.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field. Relevant experience may be considered in lieu of a degree.

Experience: 5-10 years of experience in DevOps, cloud infrastructure management, and machine learning infrastructure.

Required Skills:

Strong experience with AWS services, including AWS Batch, SLURM, EKS, and GPU-optimized workloads.
Proficiency in Terraform and Docker.
Experience with CI/CD pipelines, Jenkins, and GitLab.
Knowledge of machine learning infrastructure and managing large-scale training environments.
Familiarity with serverless architectures and Lambda.
Experience with DevOps tooling, such as GitLab CI and CircleCI.

Preferred Skills:

Expertise in GPU utilization and optimizing hardware resources.
Knowledge of additional DevOps tooling and best practices.
Ability to contribute to engineering tasks and the development of machine learning systems.

📝 Enhancement Note: Candidates should have a strong background in DevOps principles, AWS management, and machine learning infrastructure to excel in this role.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience with AWS services, Terraform, Docker, and CI/CD pipelines.
Showcase projects that involve managing and optimizing machine learning workflows.
Highlight your ability to collaborate with data scientists and ensure high availability and performance of AI platform infrastructure.

Technical Documentation:

Provide clear and concise documentation for your projects, including code quality, commenting, and version control strategies.
Include performance metrics, testing methodologies, and optimization techniques used in your projects.

📝 Enhancement Note: As this role involves managing and optimizing AI platform infrastructure, candidates should emphasize their experience with AWS services, machine learning workflows, and collaboration with data scientists in their portfolios.

💵 Compensation & Benefits

Salary Range: €50,000 - €70,000 per year (based on regional market research and experience level)

Benefits:

Competitive salary and benefits package.
Opportunity to work with smart humans, meaningful brands, and cool tools in a dynamic and forward-thinking environment.
Comprehensive Employee Resource Groups focusing on environmental, women empowerment, charitable initiatives, diversity, equity, and inclusion.
Support, tools, and development opportunities to excel in your role.

Working Hours: Full-time, 40 hours per week, with flexible deployment windows and maintenance windows.

📝 Enhancement Note: The salary range is based on regional market research and experience level, with a focus on competitive compensation for DevOps engineers with machine learning infrastructure experience.

🎯 Team & Company Context

🏢 Company Culture

Industry: Advertising technology and media.

Company Size: Large (1,000+ employees).

Founded: 2011 (as Teads), with a merger history including Outbrain in 2021.

Team Structure:

The AI Platform team is part of the larger Engineering organization, collaborating with data scientists, product managers, and other engineering teams.
The team consists of DevOps engineers, data engineers, and data scientists, working together to manage and optimize AI platform infrastructure.

Development Methodology:

Agile development methodologies, including sprint planning, code reviews, and continuous integration/continuous deployment (CI/CD) pipelines.
Collaborative development environment, fostering innovation, creative problem-solving, and continuous learning.

Company Website: www.teads.com

📝 Enhancement Note: Teads offers a dynamic and forward-thinking environment, fostering collaboration and innovation in the advertising technology and media industry.

📈 Career & Growth Analysis

Web Technology Career Level: Senior DevOps Engineer, focusing on AI platform infrastructure management and optimization.

Reporting Structure: Reports directly to the AI Platform Engineering Manager, collaborating with data scientists, product managers, and other engineering teams.

Technical Impact: Ensures high availability, scalability, and performance of AI platform infrastructure, driving meaningful business outcomes for branding and performance objectives.

Growth Opportunities:

Technical leadership and architecture decision-making opportunities as the AI Platform team grows and evolves.
Expansion into emerging technologies and machine learning infrastructure management.
Potential career progression into senior engineering or management roles within the AI Platform team or broader Engineering organization.

📝 Enhancement Note: This role offers significant growth potential, with a focus on technical leadership, architecture decision-making, and emerging technology adoption within the AI Platform team.

🌐 Work Environment

Office Type: Modern, collaborative office space with state-of-the-art technology and ergonomic workstations.

Office Location(s): Ljubljana, Slovenia, with additional global locations available for remote work arrangements.

Workspace Context:

Collaborative workspace with dedicated areas for team meetings, brainstorming sessions, and quiet focus time.
Access to multiple monitors, testing devices, and development tools tailored to web development and server administration tasks.
Cross-functional collaboration opportunities with designers, marketers, and other teams to ensure user-focused and impactful AI platform solutions.

Work Schedule: Flexible work schedule with core hours and remote work options, accommodating project deadlines and maintenance windows.

📝 Enhancement Note: Teads offers a modern, collaborative work environment with flexible scheduling options, fostering cross-functional collaboration and user-focused AI platform solutions.

📄 Application & Technical Interview Process

Interview Process:

Technical Preparation: Brush up on AWS services, Terraform, Docker, and CI/CD pipelines. Familiarize yourself with machine learning infrastructure and serverless architectures.
Online Assessment: Complete an online assessment focusing on AWS services, Terraform, and Docker.
Technical Deep Dive: Participate in a technical deep dive, discussing your approach to AI platform infrastructure management and optimization.
Final Interview: Meet with the hiring manager and other team members to discuss your fit for the role, career goals, and expectations.

Portfolio Review Tips:

Highlight your experience with AWS services, Terraform, Docker, and CI/CD pipelines.
Showcase projects that demonstrate your ability to manage and optimize machine learning workflows.
Emphasize your collaboration with data scientists and user-focused AI platform solutions.

Technical Challenge Preparation:

Practice AWS service management, Terraform configuration, and Docker container orchestration.
Familiarize yourself with machine learning infrastructure and serverless architectures.
Prepare for problem-solving questions focused on AI platform infrastructure management and optimization.

ATS Keywords: AWS, Terraform, Docker, CI/CD, Machine Learning, Infrastructure Management, Serverless Architecture, DevOps, Cloud Computing, Data Pipeline, AI Tools, Business Metrics, Pricing, Collaboration, Performance Optimization, Cost Reduction, Security, Agile Methodologies, AI Platform, Advertising Technology, Media.

📝 Enhancement Note: Prepare for a comprehensive interview process focused on AWS services, Terraform, Docker, and machine learning infrastructure management, with a strong emphasis on collaboration and user-focused AI platform solutions.

📌 Application Steps

To apply for this DevOps Engineer for AI Platform position:

Submit your application through the application link provided.
Customize your portfolio to highlight your experience with AWS services, Terraform, Docker, and CI/CD pipelines, emphasizing your ability to manage and optimize machine learning workflows.
Optimize your resume for web technology roles, emphasizing your project highlights and technical skills.
Prepare for the technical interview process, focusing on AWS services, Terraform, Docker, and machine learning infrastructure management.
Research Teads' company culture, focusing on their commitment to innovation, collaboration, and user-focused AI platform solutions.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web technology industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

DevOps Engineer for AI Platform