Senior AI PLATFORM ENGINEER (Domino Data Labs, Databricks)

NEORIS
Full_time•Madrid, Spain

📍 Job Overview

  • Job Title: Senior AI Platform Engineer (Domino Data Labs, Databricks)
  • Company: NEORIS
  • Location: Madrid, Spain
  • Job Type: Hybrid (Remote with 1 office day per month)
  • Category: AI & Machine Learning, Cloud Infrastructure, DevOps
  • Date Posted: 2025-06-19
  • Experience Level: 10+ years - Hands on

🚀 Role Summary

  • Design, develop, and maintain robust AI infrastructure to support machine learning workflows.
  • Leverage modern cloud technologies and practices to enhance AI capabilities.
  • Collaborate with cross-functional teams to integrate AI solutions into the AWS cloud infrastructure.
  • 📝 Enhancement Note: This role requires a strong background in AI platform engineering, with a focus on infrastructure as code, cloud services, and machine learning workflows.

💻 Primary Responsibilities

  • 📝 Enhancement Note: The following responsibilities are tailored to AI platform engineering, with a focus on cloud infrastructure, machine learning, and data science at scale.

  • Design and implement scalable AI platform solutions to support machine learning workflows.

  • Experience building and delivering software using the Python programming language, with exceptional ability in other programming languages.

  • Demonstratable experience deploying the underlying infrastructure and tooling for running machine learning or data science at scale using infrastructure as code.

  • Experience using DevOps to enable automation strategies.

  • Experience or awareness of MLOps practices and building pipelines to accelerate and automate machine learning.

  • Manage and optimize the deployment of applications on Amazon EKS (Elastic Kubernetes Service).

  • Implement infrastructure as code using tools like Terraform or AWS CloudFormation.

  • Provision and scale AI platforms such as Domino Data Labs, Databricks, or similar systems.

  • Collaborate with cross-functional teams to integrate AI solutions into the AWS cloud infrastructure.

  • Drive automation and develop DevOps pipelines using GitHub and GitHub Actions.

  • Ensure high availability and reliability of AI platform services.

  • Monitor and troubleshoot system performance, providing quick resolutions.

  • Stay updated with the latest industry trends and advancements in AI and cloud technologies.

  • Experience working with GxP compliant life science systems will be looked upon favorably.

🎓 Skills & Qualifications

Education:

  • Relevant degree in Computer Science, Engineering, or a related field with a focus on AI, machine learning, or data science.

Experience:

  • Proven hands-on experience with Amazon EKS and AWS cloud services.
  • Strong expertise in infrastructure as code with Terraform and AWS CloudFormation.
  • Strong expertise with Python programming.
  • Experience in provisioning and scaling AI platforms like Domino Data Labs, Databricks, or similar systems.
  • Solid understanding of DevOps principles and experience with CI/CD tools like GitHub Actions.
  • Familiarity with version control using Git and GitHub.
  • Excellent problem-solving skills and the ability to work independently and in a team.
  • Strong communication and collaboration skills.

📊 Web Portfolio & Project Requirements

  • 📝 Enhancement Note: Portfolio requirements are tailored to AI platform engineering, focusing on cloud infrastructure, machine learning, and data science projects.

  • Portfolio Essentials:

    • Demonstrate experience in designing, developing, and maintaining AI infrastructure.
    • Showcase projects that highlight your ability to manage and optimize the deployment of applications on Amazon EKS.
    • Include examples of implementing infrastructure as code using tools like Terraform or AWS CloudFormation.
    • Display your experience in provisioning and scaling AI platforms such as Domino Data Labs, Databricks, or similar systems.

  • Technical Documentation:

    • Provide documentation showcasing your ability to design and implement scalable AI platform solutions.
    • Include examples of your experience using DevOps to enable automation strategies.
    • Demonstrate your understanding of MLOps practices and building pipelines to accelerate and automate machine learning.

💵 Compensation & Benefits

Salary Range:

  • 📝 Enhancement Note: Salary range is estimated based on market research for AI platform engineering roles in Madrid, Spain, with 10+ years of experience.

  • €70,000 - €90,000 per year (gross)

Benefits:

  • Competitive Salaries
  • Career Development Plan
  • Social Benefits
  • Continuous Training
  • Flexible Hours

Working Hours:

  • Full-time (40 hours/week) with 1 office day per month in Barcelona's central office.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology consulting and digital transformation services.

Company Size: Medium (250-999 employees)

Founded: 2002

Team Structure:

  • The AI & Machine Learning team works closely with cross-functional teams, including data engineers, data scientists, and software developers.
  • The team follows Agile methodologies, with a focus on collaboration, continuous integration, and delivery.

Development Methodology:

  • The team uses Scrum for project management, with sprint planning, daily stand-ups, and regular retrospectives.
  • They follow best practices for code review, testing, and quality assurance.
  • CI/CD pipelines are used for automated deployment and continuous integration.

Company Website: www.neoris.com

📝 Enhancement Note: NEORIS focuses on digital transformation and technology consulting, with a strong emphasis on collaboration and continuous improvement. The company values innovation, agility, and customer-centricity.

📈 Career & Growth Analysis

AI Platform Engineering Career Level:

  • This role is at the senior level, with a focus on designing, developing, and maintaining robust AI infrastructure. The role requires a deep understanding of AI platform engineering, cloud services, and machine learning workflows.

Reporting Structure:

  • The Senior AI Platform Engineer reports directly to the AI & Machine Learning Team Lead and collaborates with cross-functional teams, including data engineers, data scientists, and software developers.

Technical Impact:

  • The role has a significant impact on the design, development, and maintenance of AI infrastructure, enabling machine learning workflows and data science at scale.
  • The role also influences the integration of AI solutions into the AWS cloud infrastructure and the optimization of system performance.

Growth Opportunities:

  • Technical Growth: Deepen expertise in AI platform engineering, cloud services, and machine learning workflows.
  • Leadership Growth: Develop leadership skills by mentoring junior team members and contributing to team decision-making processes.
  • Career Transition: Explore opportunities in technology or management roles within the organization.

📝 Enhancement Note: This role offers significant growth opportunities in both technical and leadership domains. The role's focus on AI platform engineering, cloud services, and machine learning workflows provides a strong foundation for career advancement.

🌐 Work Environment

Office Type: Hybrid (Remote with 1 office day per month)

Office Location(s):

  • Barcelona, Spain (BCN Centro)

Workspace Context:

  • The remote work environment allows for flexibility and better work-life balance.
  • The office space in Barcelona's central office is designed to foster collaboration and creativity.
  • The team uses modern development tools, multiple monitors, and testing devices to ensure high-quality work.

Work Schedule:

  • Full-time (40 hours/week) with flexible hours and the possibility of remote work.
  • The role requires occasional on-site presence in Barcelona's central office (1 day per month).

📝 Enhancement Note: The hybrid work environment at NEORIS offers a balance between remote work and in-office collaboration. The company provides a modern workspace with the necessary tools to ensure high-quality work.

📄 Application & Technical Interview Process

Interview Process:

  • Phone/Screening: A brief phone call or video conference to discuss your experience and motivation for the role.
  • Technical Assessment: A hands-on technical assessment focusing on AI platform engineering, cloud services, and machine learning workflows.
  • Behavioral Interview: A structured interview to assess your problem-solving skills, communication, and collaboration abilities.
  • Final Decision: A final decision based on your technical skills, cultural fit, and alignment with the company's values.

Portfolio Review Tips:

  • Highlight your experience in designing, developing, and maintaining AI infrastructure.
  • Showcase your ability to manage and optimize the deployment of applications on Amazon EKS.
  • Demonstrate your expertise in implementing infrastructure as code using tools like Terraform or AWS CloudFormation.
  • Include examples of your experience in provisioning and scaling AI platforms such as Domino Data Labs, Databricks, or similar systems.

Technical Challenge Preparation:

  • Brush up on your knowledge of AI platform engineering, cloud services, and machine learning workflows.
  • Familiarize yourself with the latest trends and best practices in AI infrastructure development.
  • Prepare for hands-on challenges that focus on designing, developing, and maintaining AI infrastructure.

ATS Keywords:

  • AI Platform Engineering
  • Machine Learning
  • Python
  • Infrastructure as Code
  • DevOps
  • MLOps
  • Amazon EKS
  • Terraform
  • AWS CloudFormation
  • GitHub
  • GitHub Actions
  • Collaboration
  • Problem-Solving
  • Cloud Technologies
  • System Performance Monitoring
  • Life Science Systems

📝 Enhancement Note: The interview process for this role focuses on assessing your technical skills in AI platform engineering, cloud services, and machine learning workflows. The portfolio review and technical challenge preparation tips are tailored to help you showcase your expertise in these areas.

🛠 Technology Stack & Web Infrastructure

Cloud Platforms:

  • Amazon Web Services (AWS)
    • Amazon EKS (Elastic Kubernetes Service)
    • AWS CloudFormation
    • AWS Lambda
    • Amazon S3
    • Amazon RDS
    • Amazon Redshift

AI Platforms:

  • Domino Data Labs
  • Databricks
  • Other similar systems (e.g., Kubeflow, MLflow)

Programming Languages:

  • Python
  • Other programming languages (as needed)

Infrastructure as Code Tools:

  • Terraform
  • AWS CloudFormation

Version Control:

  • Git
  • GitHub

CI/CD Tools:

  • GitHub Actions
  • AWS CodePipeline
  • AWS CodeBuild

Monitoring Tools:

  • AWS CloudWatch
  • Prometheus
  • Grafana

📝 Enhancement Note: The technology stack for this role focuses on AWS cloud services, AI platforms, and infrastructure as code tools. Familiarity with these technologies is essential for success in this role.

👥 Team Culture & Values

AI Platform Engineering Values:

  • Innovation: Continuously explore and implement new technologies to improve AI infrastructure.
  • Collaboration: Work closely with cross-functional teams to integrate AI solutions into the AWS cloud infrastructure.
  • Reliability: Ensure high availability and reliability of AI platform services.
  • Performance: Monitor and optimize system performance to meet the demands of machine learning workflows.

Collaboration Style:

  • Cross-functional Integration: Work closely with data engineers, data scientists, and software developers to integrate AI solutions into the AWS cloud infrastructure.
  • Code Review Culture: Follow best practices for code review, testing, and quality assurance.
  • Knowledge Sharing: Regularly share knowledge and experiences with team members to foster continuous learning and improvement.

📝 Enhancement Note: The AI platform engineering team at NEORIS values innovation, collaboration, reliability, and performance. The team fosters a culture of knowledge sharing and continuous learning to ensure high-quality work.

⚡ Challenges & Growth Opportunities

Technical Challenges:

  • Scalability: Design and implement scalable AI platform solutions to support machine learning workflows.
  • Performance Optimization: Monitor and optimize system performance to meet the demands of machine learning workflows.
  • Cloud Migration: Migrate existing AI infrastructure to AWS cloud services, optimizing for cost, performance, and scalability.
  • Emerging Technologies: Stay updated with the latest trends and advancements in AI and cloud technologies, and integrate them into the AI infrastructure as needed.

Learning & Development Opportunities:

  • Technical Skill Development: Deepen your expertise in AI platform engineering, cloud services, and machine learning workflows through workshops, online courses, and conferences.
  • Leadership Development: Develop your leadership skills through mentoring, coaching, and team decision-making processes.
  • Architecture Decision-Making: Contribute to team decision-making processes related to AI infrastructure architecture and design.

📝 Enhancement Note: The technical challenges and learning opportunities for this role focus on AI platform engineering, cloud services, and machine learning workflows. These challenges and opportunities provide a strong foundation for career growth and development.

💡 Interview Preparation

Technical Questions:

  • AI Platform Engineering: Design and implement scalable AI platform solutions to support machine learning workflows.
  • Cloud Services: Manage and optimize the deployment of applications on Amazon EKS (Elastic Kubernetes Service).
  • Infrastructure as Code: Implement infrastructure as code using tools like Terraform or AWS CloudFormation.
  • System Performance Monitoring: Monitor and optimize system performance to meet the demands of machine learning workflows.
  • MLOps: Experience or awareness of MLOps practices and building pipelines to accelerate and automate machine learning.

Company & Culture Questions:

  • Company Culture: Describe your understanding of NEORIS's company culture and how you would contribute to it.
  • Collaboration: Explain your experience working with cross-functional teams and how you would collaborate with data engineers, data scientists, and software developers.
  • Problem-Solving: Describe a complex technical challenge you faced in the past and how you overcame it.

Portfolio Presentation Strategy:

  • AI Platform Engineering: Highlight your experience in designing, developing, and maintaining AI infrastructure.
  • Cloud Infrastructure: Showcase your ability to manage and optimize the deployment of applications on Amazon EKS.
  • Infrastructure as Code: Demonstrate your expertise in implementing infrastructure as code using tools like Terraform or AWS CloudFormation.
  • System Performance Monitoring: Include examples of your experience monitoring and optimizing system performance for machine learning workflows.

📝 Enhancement Note: The interview preparation tips for this role focus on AI platform engineering, cloud services, and machine learning workflows. These tips are designed to help you showcase your expertise in these areas and demonstrate your fit for the role.

📌 Application Steps

To apply for this Senior AI Platform Engineer (Domino Data Labs, Databricks) position:

  • Submit your application through the application link here.
  • Customize your portfolio with live demos and responsive examples showcasing your experience in AI platform engineering, cloud services, and machine learning workflows.
  • Optimize your resume for AI & Machine Learning roles, highlighting your technical skills and project accomplishments.
  • Prepare for technical interviews by brushing up on your knowledge of AI platform engineering, cloud services, and machine learning workflows.
  • Research NEORIS, focusing on their AI & Machine Learning team, company culture, and user experience understanding.

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

Application Requirements

Candidates should have proven hands-on experience with Amazon EKS and AWS cloud services, along with strong expertise in Infrastructure as Code and Python programming. Familiarity with DevOps principles and CI/CD tools is also essential.