AI Engineer

About Total

Total is a major player in the energy and chemical sector, present on five continents and operating in over 130 countries with nearly 100,000 employees. Our activities span Exploration & Production of oil and gas, Refining & Chemicals, New Energies, Marketing & Services, and Trading & Shipping. These operations are supported by functional departments such as Finance, Legal, Strategy, Information Systems, Human Resources, and Communication.

Our strategy is built on ethics, safety, and environmental and social responsibility, focusing on profitable and sustainable growth, competitive refining and petrochemical platforms, innovative customer solutions, and investment in solar and biomass for the future.

Your Role

You will be integrated into the Data Science and AI team, a group of approximately forty experts dedicated to developing and deploying data and AI solutions at the heart of the Digital Factory's products for the company's business units.

AI Engineer Responsibilities:

Industrialization and Production Deployment of AI Models

• Take charge of industrializing complex or high-stakes AI solutions.
• Design and evolve technical architectures for deploying and operating AI models.
• Manage technical choices related to implementation (tools, frameworks, patterns).
• Anticipate performance, security, robustness, and maintainability constraints from the design phase.
• Guarantee the overall quality of AI components within your scope.
• Possess in-depth knowledge of cloud infrastructure and deploying AI-based products.

Operation, Reliability, and MLOps

• Define, optimize, and evolve MLOps pipelines (automation, advanced monitoring, retraining).
• Implement advanced monitoring, alerting, and drift management mechanisms.
• Take operational responsibility for critical or large-scale AI systems.
• Anticipate and address technical risks related to operating AI systems.
• Guarantee the long-term reliability, security, and scalability of AI solutions.

Collaboration with Product and Technical Teams

• Play a key role in scoping and technical arbitration phases for AI use cases.
• Manage technical exchanges with Data, IT, Product, and Platform teams.
• Contribute to structuring decisions related to architectures, tools, and operating methods.
• Serve as a technical referent for your scope.
• Ensure the technical consistency of deployed AI solutions.

Standards, Continuous Improvement, and Knowledge Sharing

• Actively contribute to defining and evolving AI Engineering and MLOps standards.
• Capitalize on feedback and structure best practices.
• Support collective technical maturity growth.
• Share expertise through mentoring, code reviews, and supporting less experienced profiles.
• Contribute to training, onboarding, and recruitment actions for technical AI profiles.

Qualifications

Education Level:

• Bac +5 / Master's or equivalent level in an engineering school or in applied mathematics, data science, or AI.

Technical Skills:

• Experience in designing and developing Data / AI solutions and deploying them in production.
• Proficiency in Python is necessary; TypeScript is a plus.
• Mastery of software development best practices (code quality, testing, documentation) to transform validated models into robust industrial solutions.
• Ability to integrate and deploy artificial intelligence models into the information system as services (API, batch, streaming), in coordination with Data, IT, and Product teams.
• Mastery of AI Engineering and MLOps practices (CI/CD, infrastructure, monitoring, incident management) to ensure the reliability, performance, and sustainability of AI systems in production.
• Good knowledge of data and AI environments (e.g., Databricks) and cloud platforms (Azure and/or AWS), with an understanding of security, performance, and scalability challenges.
• Ability to apply and evolve AI Engineering and MLOps standards, capitalize on feedback, and contribute to collective technical maturity.

Behavioral Skills:

• Teamwork, Communication, Ability to assert oneself and say no when necessary, pedagogical skills and ability to explain achievements to any audience, curiosity and proactivity.

Language Skills:

• Fluent French & practical English.

Certifications:

• Cloud certification is a plus (AWS or Azure).

Specific Knowledge:

• Experience in an industrial context is appreciated (logistics, stock management, industrial maintenance, sensor data analysis, process analysis and modeling).

Experience Level:

• Minimum 8 years of experience in the development and deployment of agentic AI applications, Machine learning models within the framework of industrial projects (deployment and production management of models).