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GenAI Engineering Consultant

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Junagadh Not disclosed
2 days ago
Freelance/Part-Time
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About the job

Hiring: GenAI Engineering Consultant (Freelance / Part-Time) We are looking for an experienced GenAI Engineering Consultant to work with us on a freelance or part-time basis and help architect and execute a complete GenAI business and technical roadmap. What We’re Looking For • Minimum 5+ years of experience in GenAI / LLM-based systems • Proven experience in building and deploying GenAI models from scratch • Strong understanding of LLM training pipelines • Deep experience in: Data collection & dataset structuring Data annotation frameworks Model fine-tuning (SFT, RLHF, etc.) Prompt engineering Evaluation frameworks Model optimization & deployment • Prior experience in a startup environment • Ability to design and execute end-to-end AI workflows Scope of Work The selected consultant will: • Help define the GenAI business model strategy • Design and build LLM-based models from scratch • Establish data annotation and quality control guidelines • Architect complete training pipelines (from dataset creation to fine-tuning) • Define evaluation metrics and model improvement workflows • Build an end-to-end GenAI workflow — from model creation to client-ready deployment • Guide the team in setting up scalable GenAI infrastructure Tech Expectations Experience with: • Transformer architectures • Open-source LLMs (LLaMA, Mistral, etc.) • Fine-tuning frameworks • Vector databases • RAG systems • Model deployment pipelines • AI evaluation and alignment methodologies Engagement Type • Freelance / Part-Time • Flexible working arrangement • High-impact strategic role If you have successfully built GenAI systems from scratch and can guide a team through the full lifecycle — from model creation to client delivery — we would love to connect.

Requirements

  • GenAI
  • LLM
  • data collection
  • dataset structuring
  • data annotation
  • model fine-tuning
  • prompt engineering

Preferred Technologies

  • GenAI
  • LLM
  • data collection
  • dataset structuring
  • data annotation
  • model fine-tuning
  • prompt engineering

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