📍 Job Overview

• Job Title: AI Platform Engineer (Data DevOps)
• Company: NEORIS
• Location: Spain
• Job Type: Hybrid
• Category: DevOps Engineer
• Date Posted: 2025-06-27
• Experience Level: 10+
• Remote Status: On-site/Hybrid

🚀 Role Summary

• Key Responsibilities: Manage cloud infrastructure and cost control, design CI/CD pipelines, ensure infrastructure scalability and resource optimization, conduct technical research, troubleshoot issues, and collaborate with AI Ops roles to guarantee platform performance and reliability.
• Key Skills: Cloud Infrastructure, Cost Control, AWS, Terraform, Python, CI/CD Pipelines, AI Model Training, Deployment, Scalability, Resource Optimization, Troubleshooting, Collaboration, Performance, Reliability, Detail-Oriented, Quality.

💻 Primary Responsibilities

🛠️ Infrastructure Management & Cost Control

• Manage cloud infrastructure and cost control, with a strong focus on AWS, using Terraform and Python for automation and optimization. Ensure efficient resource utilization and minimize expenses without compromising performance.

🔧 CI/CD Pipeline Development & Maintenance

• Design, develop, and maintain robust CI/CD pipelines for AI model training and deployment. Implement best practices for continuous integration and continuous deployment to streamline the development lifecycle and ensure smooth, reliable releases.

📈 Infrastructure Scalability & Resource Optimization

• Ensure the infrastructure's scalability to meet increasing demands and optimize resource allocation to maximize efficiency. Implement auto-scaling, load balancing, and other techniques to maintain optimal performance under varying workloads.

🔍 Technical Research & Analysis

• Conduct research and analyze technical findings to propose optimized solutions. Stay up-to-date with the latest trends and best practices in AI platform engineering and apply them to improve existing systems and processes.

🛠️ Troubleshooting & Incident Management

• Troubleshoot issues and ensure smooth deployments with minimal service disruption. Collaborate with cross-functional teams to resolve complex problems and implement preventative measures to minimize future incidents.

⚙️ Platform Performance & Reliability

• Work closely with AI Ops roles to guarantee platform performance and reliability. Monitor key performance indicators (KPIs), set up alerts, and implement proactive measures to maintain high availability and minimize downtime.

🎓 Skills & Qualifications

🎓 Education & Experience

• Education: Bachelor's degree in Computer Science, Engineering, or a related field. Relevant certifications in cloud platforms, DevOps, or AI are a plus.
• Experience: A minimum of 10 years of professional experience in similar roles, with a strong background in AI platform engineering, cloud infrastructure management, and CI/CD pipelines.

🛠️ Required Skills

• Cloud Infrastructure: Proficiency in managing cloud infrastructure, particularly with AWS.
• Infrastructure as Code (IaC): Strong experience with Terraform and familiarity with other IaC tools like CloudFormation or Azure Resource Manager.
• Programming: Solid proficiency in Python for scripting, automation, and data analysis.
• CI/CD Pipelines: Extensive experience with CI/CD pipelines, using tools like Jenkins, GitLab CI/CD, or CircleCI.
• AI Platform Engineering: Deep understanding of AI model training and deployment pipelines, with experience in MLOps or similar roles.
• Troubleshooting: Excellent problem-solving skills and the ability to diagnose and resolve complex technical issues.
• Collaboration: Strong communication and teamwork skills, with experience working in cross-functional teams.

🌟 Preferred Skills

• Containerization & Orchestration: Experience with Kubernetes, Docker, or other containerization technologies.
• Monitoring & Logging: Familiarity with monitoring tools like Prometheus, Grafana, or ELK Stack.
• Infrastructure Automation: Experience with configuration management tools like Ansible, Puppet, or Chef.
• AI/ML Frameworks: Knowledge of AI/ML frameworks like TensorFlow, PyTorch, or scikit-learn.
• Agile Methodologies: Experience working in Agile environments and familiarity with Scrum or Kanban.

📊 Web Portfolio & Project Requirements

📚 Portfolio Essentials

• Cloud Infrastructure Projects: Showcase your experience in managing cloud infrastructure, highlighting your ability to optimize resources and minimize costs.
• CI/CD Pipeline Projects: Demonstrate your expertise in designing, developing, and maintaining CI/CD pipelines, with a focus on AI model training and deployment.
• AI Platform Projects: Highlight your experience in AI platform engineering, including model training, deployment, and monitoring.
• Troubleshooting & Incident Management: Include examples of complex technical issues you've resolved and the steps you took to prevent similar incidents in the future.

📝 Technical Documentation

• Code Quality & Documentation: Provide clear, concise, and well-commented code, with up-to-date documentation that explains the purpose, functionality, and usage of key components.
• Version Control & Deployment Processes: Demonstrate your familiarity with version control systems like Git, and explain your deployment processes, including branching strategies, pull requests, and code reviews.
• Testing & Performance Optimization: Showcase your experience in testing methodologies, performance metrics, and optimization techniques, with a focus on AI platform engineering.

💵 Compensation & Benefits

💰 Salary Range

• Estimated Salary Range: €60,000 - €80,000 per year (Gross), based on market research for similar roles in Spain with 10+ years of experience in AI platform engineering and cloud infrastructure management.

🎁 Benefits

• Contract: Permanent contract with a competitive salary.
• Career Development: A personalized career development plan, whether you want to grow technically or into leadership roles.
• Social Benefits Package: Comprehensive social benefits package.
• Flexible Working Hours: Flexible working hours to support your work-life balance.
• Hybrid Work Model: Hybrid work model, combining on-site and remote work.

🎯 Team & Company Context

🏢 Company Culture

🌐 Industry & Size

• Industry: Information Technology and Services, with a focus on AI and data-driven solutions.
• Company Size: Medium-sized company with over 5,000 employees worldwide, providing specialized technical services to various industries.

📅 Founded & Growth

• Founded: 1995, with over 25 years of experience in the industry.
• Growth: Constantly growing, with a strong focus on innovation and improvement.

🤝 Team Structure & Collaboration

• Team Size & Specialization: Medium-sized teams with specialized roles in AI, data engineering, and DevOps.
• Reporting Structure: Flat organizational structure, with clear lines of communication and regular check-ins to ensure alignment and progress.
• Cross-Functional Collaboration: Close collaboration with AI/ML, data engineering, and product teams to deliver innovative solutions and drive business value.

🔄 Development Methodologies & Processes

• Agile/Scrum: Utilizes Agile methodologies and Scrum frameworks to manage projects and ensure efficient delivery.
• Code Review & Quality Assurance: Emphasizes code review, testing, and quality assurance practices to maintain high coding standards and minimize technical debt.
• Deployment Strategies: Implements continuous integration and continuous deployment (CI/CD) pipelines, with a focus on automated testing, deployment, and monitoring.

📈 Career & Growth Analysis

🌱 Web Technology Career Level

• Role Level: Senior-level role, requiring extensive experience in AI platform engineering, cloud infrastructure management, and CI/CD pipelines.
• Responsibility Scope: Responsible for managing cloud infrastructure, designing CI/CD pipelines, ensuring infrastructure scalability and resource optimization, conducting technical research, troubleshooting issues, and collaborating with AI Ops roles to guarantee platform performance and reliability.

👥 Reporting Structure & Team Dynamics

• Reporting Relationships: Reports directly to the AI Platform Engineering Manager, with close collaboration with AI Ops, data engineering, and product teams.
• Technical Impact: Has a significant impact on AI platform performance, reliability, and scalability, ensuring smooth deployments and minimal service disruption.

📈 Growth Opportunities

• Technical Growth: Opportunities to specialize in cutting-edge technologies, expand your skill set, and take on more complex projects as you progress in your career.
• Leadership Development: Potential to move into technical leadership roles, mentoring junior team members, and driving strategic decisions related to AI platform engineering and cloud infrastructure management.

🌐 Work Environment

🏢 Office Type & Location(s)

• Office Type: Modern, collaborative workspaces designed to foster innovation and teamwork.
• Office Location(s): Multiple offices worldwide, with a strong presence in Europe, North America, and Latin America.

🌳 Workspace Context

• Collaborative Environment: Encourages cross-functional collaboration, with dedicated spaces for team meetings, brainstorming sessions, and workshops.
• Development Tools & Equipment: Provides state-of-the-art development tools, multiple monitors, and testing devices to ensure optimal productivity and performance.
• Workplace Amenities: Offers various amenities, such as cafeterias, gyms, and relaxation areas, to support employee well-being and work-life balance.

🕒 Work Schedule

• Flexible Hours: Offers flexible working hours to accommodate individual needs and promote work-life balance.
• Deployment Windows & Maintenance: May require occasional work during off-hours to address critical issues, deploy updates, or perform maintenance tasks.

📄 Application & Technical Interview Process

🔎 Interview Process

1. Technical Preparation: Brush up on your knowledge of cloud infrastructure, AWS, Terraform, Python, and CI/CD pipelines. Familiarize yourself with AI platform engineering best practices and recent trends in the industry.
2. Online Assessment: Complete an online assessment to evaluate your technical skills and problem-solving abilities.
3. Technical Phone Screen: Participate in a technical phone screen to discuss your experience, accomplishments, and career goals. Be prepared to answer questions about your portfolio and AI platform engineering projects.
4. On-site Interview: Attend an on-site interview to meet the team, discuss the role and company culture, and demonstrate your technical skills through hands-on exercises and case studies.
5. Final Evaluation: If successful, proceed to the final evaluation stage, where you'll have the opportunity to ask questions, negotiate terms, and discuss the next steps in your career with NEORIS.

📝 Portfolio Review Tips

1. Curate Your Portfolio: Highlight your most relevant AI platform engineering projects, focusing on cloud infrastructure management, CI/CD pipeline development, and AI model training and deployment.
2. Live Demos & Walkthroughs: Prepare live demos and walkthroughs of your projects, showcasing your technical expertise and ability to explain complex concepts clearly and concisely.
3. Code Quality & Documentation: Ensure your code is well-commented, well-documented, and follows best practices for readability and maintainability.
4. Performance Optimization & Scalability: Emphasize your experience in optimizing infrastructure for performance and scalability, with a focus on cost control and resource efficiency.

💡 Technical Challenge Preparation

1. Technical Exercises: Practice technical exercises related to cloud infrastructure, AWS, Terraform, Python, and CI/CD pipelines to build your confidence and demonstrate your problem-solving skills.
2. System Design & Architecture: Brush up on your system design and architecture skills, focusing on AI platform engineering best practices and scalable, reliable infrastructure design.
3. Communication & Presentation: Develop your communication and presentation skills, with a focus on explaining technical concepts clearly and concisely to both technical and non-technical stakeholders.

🛠 Technology Stack & Web Infrastructure

🛠️ Frontend Technologies

• Not Applicable: This role focuses on backend and infrastructure technologies, with no frontend development requirements.

🛡️‍💻 Backend & Server Technologies

• Cloud Infrastructure: Proficiency in managing cloud infrastructure, with a strong focus on AWS.
• Infrastructure as Code (IaC): Experience with Terraform and familiarity with other IaC tools like CloudFormation or Azure Resource Manager.
• Programming: Solid proficiency in Python for scripting, automation, and data analysis.
• CI/CD Pipelines: Experience with CI/CD pipelines, using tools like Jenkins, GitLab CI/CD, or CircleCI.
• AI Platform Engineering: Deep understanding of AI model training and deployment pipelines, with experience in MLOps or similar roles.
• Monitoring & Logging: Familiarity with monitoring tools like Prometheus, Grafana, or ELK Stack.
• Containerization & Orchestration: Experience with Kubernetes, Docker, or other containerization technologies.

🔧 Development & DevOps Tools

• Version Control: Experience with Git and familiarity with other version control systems like SVN or Mercurial.
• CI/CD Pipelines: Proficiency in designing, developing, and maintaining CI/CD pipelines, with a focus on automated testing, deployment, and monitoring.
• Infrastructure Automation: Experience with configuration management tools like Ansible, Puppet, or Chef.
• AI/ML Frameworks: Knowledge of AI/ML frameworks like TensorFlow, PyTorch, or scikit-learn.
• Agile Methodologies: Experience working in Agile environments and familiarity with Scrum or Kanban.

👥 Team Culture & Values

🌟 Web Development Values

• Quality & Reliability: Emphasizes high-quality, reliable code and infrastructure, with a focus on minimizing technical debt and ensuring optimal performance.
• Collaboration & Knowledge Sharing: Encourages close collaboration and knowledge sharing between team members, fostering a culture of continuous learning and improvement.
• Innovation & Creativity: Values innovation and creativity, with a strong emphasis on staying up-to-date with the latest trends and best practices in AI platform engineering and cloud infrastructure management.
• Customer Focus: Prioritizes customer satisfaction, ensuring that AI platform engineering solutions meet business needs and drive value for clients.

🤝 Collaboration Style

• Cross-Functional Integration: Encourages close collaboration between AI platform engineering, data engineering, and product teams, with a focus on delivering innovative solutions and driving business value.
• Code Review Culture: Emphasizes code review, with a focus on maintaining high coding standards and minimizing technical debt.
• Peer Programming & Mentoring: Fosters a culture of peer programming and mentoring, with regular knowledge-sharing sessions and technical workshops.

🌱 Challenges & Growth Opportunities

🛠️ Technical Challenges

• Cloud Infrastructure Management: Stay up-to-date with the latest trends and best practices in cloud infrastructure management, with a focus on cost control, scalability, and resource optimization.
• AI Platform Engineering: Continuously expand your knowledge of AI model training, deployment, and monitoring, with a focus on emerging technologies and best practices.
• Performance Optimization & Scalability: Develop your expertise in optimizing infrastructure for performance and scalability, with a focus on cost control and resource efficiency.
• Troubleshooting & Incident Management: Enhance your problem-solving skills and ability to diagnose and resolve complex technical issues, with a focus on minimizing service disruption and ensuring high availability.

🌱 Learning & Development Opportunities

• Technical Skill Development: Continuously expand your knowledge of AI platform engineering, cloud infrastructure management, and emerging technologies through online courses, workshops, and certifications.
• Conference Attendance & Networking: Attend industry conferences, meetups, and webinars to stay informed about the latest trends, best practices, and networking opportunities in AI platform engineering and cloud infrastructure management.
• Technical Leadership & Architecture: Develop your leadership skills and expertise in AI platform engineering architecture, with a focus on driving strategic decisions and mentoring junior team members.

💡 Interview Preparation

💭 Technical Questions

1. Cloud Infrastructure & AWS: Demonstrate your proficiency in managing cloud infrastructure, with a focus on AWS, Terraform, and Python.
2. CI/CD Pipelines: Explain your experience in designing, developing, and maintaining CI/CD pipelines, with a focus on automated testing, deployment, and monitoring.
3. AI Platform Engineering: Discuss your knowledge of AI model training, deployment, and monitoring, with a focus on emerging technologies and best practices.
4. Troubleshooting & Incident Management: Describe your problem-solving approach and experience in diagnosing and resolving complex technical issues.
5. System Design & Architecture: Present your system design and architecture skills, with a focus on AI platform engineering best practices and scalable, reliable infrastructure design.

🤝 Company & Culture Questions

1. Company Culture & Values: Research the company's culture, values, and mission, and discuss how they align with your personal beliefs and career goals.
2. AI Platform Engineering Methodologies: Explain your understanding of AI platform engineering methodologies, with a focus on Agile, Scrum, and CI/CD pipelines.
3. User Experience & Performance Optimization: Describe your approach to optimizing AI platform performance and user experience, with a focus on accessibility, scalability, and cost control.
4. Team Collaboration & Communication: Discuss your experience working in cross-functional teams and your ability to communicate technical concepts clearly and concisely to both technical and non-technical stakeholders.

📝 Portfolio Presentation Strategy

1. Live Demos & Walkthroughs: Prepare live demos and walkthroughs of your AI platform engineering projects, showcasing your technical expertise and ability to explain complex concepts clearly and concisely.
2. Code Quality & Documentation: Ensure your code is well-commented, well-documented, and follows best practices for readability and maintainability.
3. Performance Optimization & Scalability: Emphasize your experience in optimizing infrastructure for performance and scalability, with a focus on cost control and resource efficiency.
4. Technical Challenges & Problem-Solving: Highlight your ability to tackle complex technical challenges and develop innovative solutions to optimize AI platform performance and reliability.

📌 Application Steps

To apply for this AI Platform Engineer (Data DevOps) position at NEORIS:

1. Customize Your Portfolio: Tailor your portfolio to showcase your most relevant AI platform engineering projects, focusing on cloud infrastructure management, CI/CD pipeline development, and AI model training and deployment.
2. Resume Optimization: Optimize your resume for AI platform engineering roles, highlighting your technical skills, experience, and accomplishments related to cloud infrastructure, AWS, Terraform, Python, and CI/CD pipelines.
3. Technical Interview Preparation: Brush up on your technical skills, practice coding challenges, and prepare for system design and architecture questions related to AI platform engineering and cloud infrastructure management.
4. Company Research: Research NEORIS's company culture, values, and mission, and prepare questions to demonstrate your understanding of the organization and its goals.

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