📍 Job Overview

Job Title: Ingénieur Développement Cloud (F/H)
Company: Thales
Location: La Ciotat, France
Job Type: On-site
Category: DevOps Engineer
Date Posted: 2025-06-18
Experience Level: Mid-Senior level (2-5 years)

🚀 Role Summary

📝 Enhancement Note: This role focuses on cloud infrastructure management, machine learning pipeline automation, and ensuring the security and compliance of AI models in production. It requires strong collaboration skills and a deep understanding of both cloud technologies and data science practices.
Lead the deployment of AI models at scale by automating ML pipelines and implementing robust CI/CD practices.
Manage cloud and on-premises infrastructures, ensuring performance, security, and cost optimization.
Foster collaboration between Data Science, DevOps, Engineering, and FinOps teams to create innovative workflows.
Monitor, trace, and ensure the compliance of AI models in production.

💻 Primary Responsibilities

📝 Enhancement Note: This role combines technical depth in cloud infrastructure management and data science with a strong emphasis on collaboration and innovation.
Cloud Infrastructure Management: Design, deploy, and maintain secure and scalable cloud infrastructure using AWS, GCP, or Azure. Ensure optimal performance, security, and cost-efficiency.
Machine Learning Pipeline Automation: Automate ML pipelines using CI/CD practices to streamline the deployment of AI models from lab to production.
Collaboration and Workflow Innovation: Work closely with Data Science, DevOps, Engineering, and FinOps teams to create and implement innovative workflows for AI model deployment and maintenance.
Model Monitoring and Compliance: Monitor AI models in production, ensuring their performance, security, and compliance with relevant standards and regulations.

🎓 Skills & Qualifications

Education: A Master's degree in Computer Science, Data Engineering, or a related field is required.

Experience: Proven experience in cloud infrastructure management, machine learning pipeline automation, and AI model deployment and maintenance is essential.

Required Skills:

Cloud infrastructure management (AWS, GCP, Azure)
Machine learning pipeline automation (CI/CD)
AI model deployment and maintenance
Infrastructure as Code (IaC) tools (Terraform, CloudFormation, etc.)
Containerization and orchestration (Docker, Kubernetes)
Infrastructure monitoring and logging (Prometheus, ELK Stack, etc.)
Security and compliance best practices
Strong collaboration and communication skills
Leadership and mentoring skills

Preferred Skills:

Experience with MLOps platforms (MLflow, Kubeflow, etc.)
Familiarity with AI/ML frameworks (TensorFlow, PyTorch, etc.)
Knowledge of infrastructure cost optimization techniques
Experience with cloud security certifications (CIS, ISO 27001, etc.)
Familiarity with Agile methodologies and DevOps practices

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

📝 Enhancement Note: Given the focus on cloud infrastructure management and AI model deployment, this role requires a portfolio that demonstrates both technical depth and collaborative prowess.
Cloud Infrastructure Projects: Showcase your experience in designing, deploying, and maintaining secure and scalable cloud infrastructure using AWS, GCP, or Azure. Highlight your ability to optimize performance, security, and cost-efficiency.
ML Pipeline Automation Projects: Demonstrate your ability to automate ML pipelines using CI/CD practices. Include examples of successful AI model deployments from lab to production.
Collaborative Projects: Highlight your experience working with cross-functional teams to create and implement innovative workflows for AI model deployment and maintenance. Include any relevant case studies or success stories.
Model Monitoring and Compliance Projects: Showcase your ability to monitor AI models in production, ensuring their performance, security, and compliance with relevant standards and regulations.

Technical Documentation:

📝 Enhancement Note: Given the collaborative nature of this role, technical documentation should emphasize clear communication, collaboration, and knowledge sharing.
Code Quality and Documentation: Demonstrate your commitment to writing clean, well-documented code. Include examples of inline comments, code reviews, and documentation standards.
Version Control and Deployment Processes: Showcase your experience with version control systems (Git) and deployment processes. Include examples of pull requests, code reviews, and deployment pipelines.
Testing Methodologies and Performance Metrics: Demonstrate your understanding of testing methodologies and performance metrics. Include examples of unit tests, integration tests, and performance benchmarks.

💵 Compensation & Benefits

Salary Range: €45,000 - €65,000 per year (based on experience and qualifications)

Benefits:

Attractive compensation package
Continuous skill development through training and career progression opportunities
Inclusive work environment that values diversity and fosters collaboration
Recognized commitment to societal and environmental responsibility

Working Hours: 40 hours per week, with flexibility for deployment windows, maintenance, and project deadlines

🎯 Team & Company Context

Company Culture:

Industry: Thales operates in the defense, aerospace, and cybersecurity industries, with a strong focus on innovation and technology.
Company Size: Thales is a large multinational corporation with over 81,000 employees worldwide. This role is based at the La Ciotat site, which focuses on identity and security technologies.
Founded: Thales was founded in 1893 and has a rich history of technological innovation and leadership.

Team Structure:

Cloud Center of Excellence: This role is part of the Cloud Center of Excellence, which focuses on driving AI adoption and innovation across the organization.
Collaborative Environment: The team consists of Data Science, DevOps, Engineering, and FinOps professionals who work together to create and implement innovative workflows for AI model deployment and maintenance.
Agile Methodologies: The team uses Agile methodologies to manage projects and foster collaboration.

Development Methodology:

CI/CD Pipelines: The team uses CI/CD pipelines to automate ML workflows and streamline AI model deployment.
Infrastructure as Code (IaC): The team uses IaC tools to manage cloud infrastructure and ensure consistency, security, and scalability.
Monitoring and Logging: The team uses monitoring and logging tools to ensure the performance, security, and compliance of AI models in production.

Company Website: Thales Group Website

📈 Career & Growth Analysis

Web Technology Career Level: This role is at the mid-senior level, focusing on cloud infrastructure management, machine learning pipeline automation, and AI model deployment and maintenance. It requires strong technical skills, leadership, and collaboration.

Reporting Structure: This role reports to the Cloud Center of Excellence manager and works closely with Data Science, DevOps, Engineering, and FinOps teams.

Technical Impact: This role has a significant impact on the deployment, performance, security, and compliance of AI models across the organization. It requires a deep understanding of both cloud technologies and data science practices.

Growth Opportunities:

Technical Leadership: This role offers opportunities for technical leadership and mentoring, as well as career progression into senior or management roles.
Emerging Technologies: Thales invests heavily in emerging technologies, providing opportunities to learn and work with the latest innovations in AI, cloud, and cybersecurity.
Global Opportunities: As a multinational corporation, Thales offers opportunities for international assignments and global career progression.

🌐 Work Environment

Office Type: Thales La Ciotat site is a modern, collaborative workspace that fosters innovation and teamwork.

Office Location(s): La Ciotat, France

Workspace Context:

Collaborative Environment: The workspace is designed to encourage collaboration and knowledge sharing between team members and across departments.
State-of-the-Art Technology: The workspace is equipped with the latest technology, including multiple monitors, testing devices, and development tools.
Flexible Working Hours: The workspace offers flexible working hours to accommodate deployment windows, maintenance, and project deadlines.

Work Schedule: The work schedule is typically 40 hours per week, with flexibility for deployment windows, maintenance, and project deadlines.

📄 Application & Technical Interview Process

Interview Process:

Technical Phone Screen: A 30-minute phone or video call to assess your technical skills and cultural fit.
On-site Technical Interview: A 2-hour on-site interview focusing on your cloud infrastructure management, machine learning pipeline automation, and AI model deployment and maintenance skills. You will be asked to complete a hands-on exercise and discuss your portfolio.
Behavioral Interview: A 30-minute interview to assess your collaboration, leadership, and problem-solving skills.
Final Decision: A final decision will be made based on your technical skills, cultural fit, and alignment with the team's goals and values.

Portfolio Review Tips:

📝 Enhancement Note: Given the focus on cloud infrastructure management and AI model deployment, your portfolio should demonstrate both technical depth and collaborative prowess.
Cloud Infrastructure Projects: Highlight your experience in designing, deploying, and maintaining secure and scalable cloud infrastructure using AWS, GCP, or Azure. Include any relevant case studies or success stories.
ML Pipeline Automation Projects: Demonstrate your ability to automate ML pipelines using CI/CD practices. Include examples of successful AI model deployments from lab to production.
Collaborative Projects: Highlight your experience working with cross-functional teams to create and implement innovative workflows for AI model deployment and maintenance. Include any relevant case studies or success stories.
Model Monitoring and Compliance Projects: Showcase your ability to monitor AI models in production, ensuring their performance, security, and compliance with relevant standards and regulations.

Technical Challenge Preparation:

📝 Enhancement Note: Given the focus on cloud infrastructure management and AI model deployment, your technical challenge preparation should focus on demonstrating your technical depth and collaborative prowess.
Cloud Infrastructure Management: Brush up on your cloud infrastructure management skills, focusing on AWS, GCP, or Azure. Familiarize yourself with the latest best practices and security standards.
Machine Learning Pipeline Automation: Review your CI/CD pipeline automation skills and ensure you are up-to-date with the latest tools and techniques.
Collaboration and Communication: Practice your collaboration and communication skills, focusing on how you can work effectively with cross-functional teams to create and implement innovative workflows for AI model deployment and maintenance.
Problem-Solving: Brush up on your problem-solving skills, focusing on how you can troubleshoot and optimize cloud infrastructure and AI model deployment and maintenance processes.

ATS Keywords:

Cloud Infrastructure Management: AWS, GCP, Azure, cloud architecture, cloud security, infrastructure as code (IaC), Terraform, CloudFormation, cost optimization
Machine Learning Pipeline Automation: CI/CD, MLflow, Kubeflow, TensorFlow, PyTorch, data science, AI/ML, deployment, production
Collaboration and Leadership: Agile, DevOps, teamwork, mentoring, innovation, problem-solving, communication
Security and Compliance: security standards, compliance, AI/ML ethics, data privacy, data protection

🛠 Technology Stack & Web Infrastructure

Cloud Infrastructure Technologies:

AWS: Amazon Web Services is a popular choice for cloud infrastructure management, offering a wide range of services for compute, storage, and networking.
GCP: Google Cloud Platform is another popular choice, offering services such as Compute Engine, Cloud Storage, and Cloud Networking.
Azure: Microsoft Azure is a comprehensive cloud platform that offers services such as Virtual Machines, Storage, and Virtual Networks.
Infrastructure as Code (IaC): IaC tools such as Terraform and CloudFormation allow for the automated provisioning and management of cloud infrastructure.

Machine Learning Technologies:

TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It is widely used for AI/ML applications and is compatible with both AWS and GCP.
PyTorch: PyTorch is another popular open-source machine learning framework developed by Facebook. It is known for its dynamic computation graph and is compatible with AWS, GCP, and Azure.
MLflow: MLflow is an open-source platform for managing the machine learning lifecycle. It is used for versioning, packaging, and serving ML models.
Kubeflow: Kubeflow is an open-source machine learning platform built on Kubernetes. It is used for deploying and managing ML workflows in a portable and scalable way.

Collaboration and Communication Tools:

Git: Git is a distributed version control system that enables collaborative software development and version tracking.
Jira: Jira is a project management and issue tracking tool used for Agile software development teams.
Confluence: Confluence is a collaboration software used to share information and knowledge within teams and organizations.
Slack: Slack is a team messaging platform that enables real-time communication and collaboration.

👥 Team Culture & Values

Web Development Values:

Innovation: Thales values innovation and encourages its employees to explore new technologies and approaches to drive AI adoption and innovation.
Collaboration: Thales fosters a collaborative work environment that encourages knowledge sharing and teamwork.
Performance Optimization: Thales values performance optimization and encourages its employees to continuously improve the performance and efficiency of AI models and cloud infrastructure.
Security and Compliance: Thales places a strong emphasis on security and compliance, ensuring that AI models and cloud infrastructure meet relevant standards and regulations.

Collaboration Style:

Cross-Functional Integration: Thales encourages cross-functional integration between teams, fostering collaboration between data scientists, engineers, and other stakeholders.
Code Review Culture: Thales values a code review culture, ensuring that code is reviewed and approved by team members before being merged into the main branch.
Peer Programming: Thales encourages peer programming, allowing team members to learn from each other and improve their skills.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Cloud Infrastructure Management: Managing cloud infrastructure at scale can be challenging, requiring a deep understanding of cloud technologies, security, and cost optimization.
Machine Learning Pipeline Automation: Automating ML pipelines can be complex, requiring a strong understanding of CI/CD practices, data science, and AI/ML frameworks.
AI Model Deployment and Maintenance: Deploying and maintaining AI models in production can be challenging, requiring a deep understanding of AI/ML, cloud infrastructure, and data science practices.
Security and Compliance: Ensuring the security and compliance of AI models and cloud infrastructure can be challenging, requiring a strong understanding of relevant standards and regulations.

Learning & Development Opportunities:

Technical Skill Development: Thales offers opportunities for technical skill development, including training and certification programs for cloud infrastructure management, machine learning, and AI/ML.
Conference Attendance: Thales encourages its employees to attend industry conferences and events, providing opportunities to learn from experts and network with peers.
Technical Mentoring: Thales offers technical mentoring programs, allowing employees to learn from experienced professionals and develop their skills.

💡 Interview Preparation

Technical Questions:

Cloud Infrastructure Management: Be prepared to discuss your experience with cloud infrastructure management, focusing on AWS, GCP, or Azure. Be ready to explain your approach to security, cost optimization, and scalability.
Machine Learning Pipeline Automation: Be prepared to discuss your experience with CI/CD pipeline automation, focusing on MLflow, Kubeflow, TensorFlow, or PyTorch. Be ready to explain your approach to automation, deployment, and production.
AI Model Deployment and Maintenance: Be prepared to discuss your experience with AI model deployment and maintenance, focusing on production, security, and compliance. Be ready to explain your approach to monitoring, troubleshooting, and optimization.

Company & Culture Questions:

Thales Culture: Be prepared to discuss your understanding of Thales' culture, values, and mission. Be ready to explain how you can contribute to Thales' success and drive AI adoption and innovation.
Agile Methodologies: Be prepared to discuss your experience with Agile methodologies, focusing on collaboration, problem-solving, and continuous improvement.
User Experience Impact: Be prepared to discuss your understanding of user experience and how you can ensure that AI models and cloud infrastructure meet the needs of users.

Portfolio Presentation Strategy:

Cloud Infrastructure Projects: Highlight your experience in designing, deploying, and maintaining secure and scalable cloud infrastructure using AWS, GCP, or Azure. Include any relevant case studies or success stories.
ML Pipeline Automation Projects: Demonstrate your ability to automate ML pipelines using CI/CD practices. Include examples of successful AI model deployments from lab to production.
AI Model Deployment and Maintenance Projects: Showcase your ability to monitor AI models in production, ensuring their performance, security, and compliance with relevant standards and regulations.
Collaboration and Leadership Projects: Highlight your experience working with cross-functional teams to create and implement innovative workflows for AI model deployment and maintenance. Include any relevant case studies or success stories.

📌 Application Steps

To apply for this web development/server administration position:

Customize Your Portfolio: Tailor your portfolio to highlight your experience with cloud infrastructure management, machine learning pipeline automation, and AI model deployment and maintenance. Include relevant case studies and success stories.
Optimize Your Resume: Optimize your resume for web technology roles, emphasizing your experience with cloud infrastructure management, machine learning, and AI/ML. Highlight your collaboration, leadership, and problem-solving skills.
Prepare for Technical Interviews: Brush up on your technical skills, focusing on cloud infrastructure management, machine learning pipeline automation, and AI model deployment and maintenance. Practice your collaboration and communication skills, focusing on how you can work effectively with cross-functional teams to create and implement innovative workflows for AI model deployment and maintenance.
Research Thales: Research Thales' culture, values, and mission. Understand how you can contribute to Thales' success and drive AI adoption and innovation.

📝 Enhancement Note: 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.

Ingénieur Développement Cloud (F/H)