AI Trainer: Civil Engineering
Soul AI · Hyderabad · onsite · Posted 37d ago
Undisclosed4–8 yrsfulltimeSourced from
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About this role
About UsSoul AI is a pioneering company founded by IIT Bombay and IIM Ahmedabad alumni, with a strong founding team from IITs, NITs, and BITS
We specialize in delivering high-quality human-curated data, AI-first scaled operations services, and more
Based in SF and Hyderabad, we are a young, fast-moving team on a mission to build AI for Good, driving innovation and positive societal impact
We are seeking experienced AI TrainerCivil Engineering with a minimum of 1 year of experience to join us in Hyderabad and contribute to impactful AI training projects
Key Responsibilities:
Create, review, and refine civil engineering questions, answers, and technical solutions
Evaluate AI-generated content for correctness in areas such as Structural Engineering, Geotechnical Engineering, Environmental Engineering, and Surveying
Identify and fix conceptual or numerical errors in AI-driven responses
Contribute to the fine-tuning of large language models using RLHF methods
Required Qualifications:
1+ year of experience in Civil Engineering (Academia, Industry Projects, Teaching, or Content Development)
Strong grasp of subjects like RCC, Steel Structures, Soil Mechanics, Transportation, and Hydraulics
Ability to critically evaluate and enhance AI-generated engineering content
Familiarity with exam-prep content (GATE, ESE) or academic curriculum is a plus
Why Join Us
Work in a competitive environment
Hands-on experience working on real-world AI training tasks
Competitive stipend (Rs
25,000- 35,000)
Shape the future of AI with Soul AI!
Sourced from naukri
Skills
HydraulicsAutoCADcStructural EngineeringgeotechnicalEnvironmental engineeringStaad ProGeotechnical EngineeringSteel StructuresconstructionTransportationsoil mechanicsSite ExecutionSite SupervisionCivil EngineeringSite Engineering
About Soul AI
Soul AI
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Soul AI provides advanced AI solutions, specializing in the curation of specialized datasets to enhance the performance of large language models globally.
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