Senior Electrical Designer
Fluence is a global leader in energy storage and grid optimization software. This role involves performing detailed electrical design for battery and inverter systems, substations, and power distribution projects. Candidates must have at least five years of experience in utility or industrial electrical design using AutoCAD and 3D modeling tools like Revit. The position requires strong technical knowledge and the ability to act as a task or project design lead.
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Experience
5+ years
Function
Engineering
Work mode
Onsite, India
Company
Tier 2
What you will work on
Fluence is a global leader in energy storage and grid optimization software. This role involves performing detailed electrical design for battery and inverter systems, substations, and power distribution projects. Candidates must have at least five years of experience in utility or industrial electrical design using AutoCAD and 3D modeling tools like Revit. The position requires strong technical knowledge and the ability to act as a task or project design lead.
Must haves
- Minimum 5 years of relevant detailed electrical design experience
- Experience with utility installations, oil and gas, or industrial plants
- Experience with drafting tools like AutoCAD
- Experience with 3D modelling tools like Revit
- Fluency in English
Tools and skills
Nice to have: german language.
About the company
Established global energy storage company with publicly traded status, fitting tier 2 criteria.
Posts mentioning Fluence
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Shitty advice from fin-fluencers
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Neural Machine Translation by Jointly Learning to Align and Translate
If you want more papers like these, drop a "+1" comment below. I will notify these people in DMs next time I upload a new paper. Bahdanau, Cho, and Bengio published a pivotal paper that reshaped the landscape of artificial intelligence, particularly in NLP. This is the first time the world was introduced to the attention mechanism, the most important thing in modern neural machine translation systems. Unlike traditional approaches that relied solely on fixed-length vector representations, the attention mechanism allowed models to dynamically focus on different parts of the input sequence during the translation process. This breakthrough not only significantly improved the accuracy of translation but also enabled the handling of longer sentences with greater fluency. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio not only innovated in the field of machine translation forward but also laid the foundation for attention-based architectures across various domains of deep learning. Their innovative approach demonstrated the power of neural networks to tackle complex sequence-to-sequence tasks and opened doors to a new era of natural language understanding and generation.