Blade Repair Engineer
Aerones
Blade Repair Engineer
Aerones
Robotic wind turbine maintenance and inspections
Aerones · Riga, Latvia · Full-time · Posted 2026-05-07
Workplace: on_site
Department: Operations
Aerones is a robotics-driven wind turbine maintenance company operating globally across Europe and the US. The full blade service portfolio includes: robotic and drone-based blade inspection, leading edge repair (LER 1–4), ice-phobic coating application, lightning protection system testing and repair, drainage hole cleaning, blade cleaning, and repeated technical checkups.
The company operates a proprietary Inspection Platform managing a large and growing fleet of turbines and blades across hundreds of clients worldwide. The platform supports the full lifecycle of inspection services from anomaly documentation through customer reporting.
This role sits within Blade Repair & Operations Excellence (BROE), part of the Global Operations Excellence function. It interfaces daily with the Operations Management Center and feeds quality data into weekly operational reviews and board-level reporting.
Strong Advantages:
We believe great work starts with feeling valued and supported. That’s why we are building an thoughtful, competitive benefits and perks to help you thrive — professionally and personally — through every step of your Career with us. You will be eligible for:
Salary from 4,000 EUR to 5,000 EUR per month (before Taxes)
After Probationary Period
Join us in Building a Cleaner, Smarter Future — one quality process improvement at a time.
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Aerones provides on-site robot-enabled wind turbine maintenance and inspection services for wind farms. Its proprietary robotic systems, including crawlers for internal inspections and robotic arms, perform blade inspections, cleaning, testing, and repairs while collecting data for analytics. Unlike traditional rope-access work, its robots operate in harsher conditions, enabling faster turnaround and broader global coverage with major customers. Its goal is to reduce turbine downtime and move toward data-driven predictive maintenance through AI analytics.
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