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Ingénieur(e) Senior Fiabilité & Maintenance Prédictive (H/F)

TotalEnergies -
Palaiseau (91)

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Détails de l'emploi

CDI
Il y a 5 jours

Description complète du poste

Contexte et environnement

Integrated Power Modelling (IPM), in OneTech Technical Line Power, supports the company’s technological ambitionin the digitalization of renewable energies value chain.

It brings together cross‑domain expertise (scientific and digital) to deliver digital products with the goal of maximizing both the performance and value of renewable assets (solar, wind,energy storage).

It is built around four platforms, each oriented toward internal business clients (GRP, REN, FPI, Trading, etc.): Origination & Design, Grid Integration, Operations Performance & Optimization, and Forecasting.

Each platform isstructured into three teams: Product Solutions (PS), Research & Capability Development (RCD) and Software Delivery& Operations (SDO).

The Operations Performance & Optimization platform’s products focus on operational efficiency and the optimization of assets, with strong Data/Cloud integration and Machine Learning/Optimisation orientation.

As Reliability & Predictive maintenance engineer, you will work in the RCD team, and interact in close collaboration with Product Managers (in PS),

Product Owners (in SDO), as well as with entities out of IPM (eg Digital Line, ITDepartment, Innovation Accelerator, TTE On…).

Activités

Define and maintain technical frameworks for reliability, predictive maintenance, and health monitoring for solar, wind,and BESS assets (asset physics, failure mechanisms, and operational behavior), ensuring consistency and robustness across technologies.
Translate the expertise into digital specifications, by defining reliability and health requirements (KPIs, degradation indicators, failure signatures, predictive signals) based on deep understanding of equipment design, operating conditions, and field feedback.
Design, develop, and validate analytical models and algorithms leveraging asset physics and data science techniques, including failure mode detection, degradation modeling, remaining useful life estimation, and automated diagnostics.
Drive advanced analytics use cases from an engineering stand point, ensuring that models and algorithms remain explainable, relevant for operations, and aligned with real asset behavior.
Act as a technical interface with digital, IT, and data teams, ensuring that engineering assumptions, analytical needs and data quality constraints are correctly understood.
Contribute to reliability and lifetime extension strategies by converting field experience, failure analyses, and model outputs into technical recommendations, standards, and design or operating improvements.
Capitalise and structure knowledge through documentation (methodologies, models, data structures, architectures,lessons learned, best practices) to ensure long‑term maintainability, traceability, and reuse of developed solutions.
Provide expert support and mentoring on reliability engineering, predictive maintenance methods, and modeling approaches to project teams and junior engineers, when required (without line or functional management responsibility).

Profil du candidat

Engineer with strong expertise in solar PV, wind, and/or BESS technologies, with proven understanding of asset behavior, failure modes, degradation mechanisms, and operational constraints.
Strong technical mindset with exposure to digital monitoring and data analytics, and the ability to work with digital experts to specify, challenge, and assess end‑to‑end technical solutions (from data acquisition to diagnostics).
Comfortable working transversally with multiple engineering disciplines and stakeholders in an international environment.
Recognized for analytical rigor, autonomy, and clear technical communication.
Curious, forward‑looking, and able to anticipate evolutions in asset monitoring, reliability methods, and renewable technologies.

Informations supplémentaires

TotalEnergies valorise la diversité, promeut le développement individuel et offre des opportunités d'emploi égales à tous les candidats.

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