AI Innovation Leader

12 hours ago

Thiruvananthapuram, Kerala, India beBeeArtificial Full time ₹ 15,00,000 - ₹ 25,00,000
GenAI Specialist Job Description

Job Overview:

We are seeking a highly skilled GenAI Specialist to drive innovation and push the boundaries of generative AI. As a key member of our team, you will design and implement advanced generative AI models to meet client needs.

Key Responsibilities:

  • Develop Next-Gen Generative AI Models: Research, design, and fine-tune state-of-the-art models like Llama, GPT, Stable Diffusion, and Variational Autoencoders (VAEs) to drive business impact.
  • Enhance AI Capabilities: Implement and optimize LLM fine-tuning, prompt engineering, and AI safety mechanisms to improve model efficiency and usability.
  • Integrate AI into Products/Services: Work closely with business and engineers to embed generative AI solutions into real-world applications, ensuring seamless user experiences.
  • Optimize for Scale & Performance: Build and refine scalable machine learning pipelines for efficient model deployment, inference, and real-time adaptation.
  • Stay Ahead of AI Trends: Keep pace with emerging technologies, model architectures, and industry breakthroughs to drive continuous innovation.

Requirements:

  • Education: Master's degree in Data Science, Statistics, Engineering or a related field (applicants with a Bachelor's degree and relevant work experience can also apply).
  • Experience: At least 4-5 years of experience in Data Science domain with strong exposure to Generative AI.
  • Skills: Experience using Machine and Deep Learning languages, preferably Python and R, to manipulate data and draw insights from large datasets.
  • Languages & Tools: Hands-on experience with LLMs, multimodal AI, and diffusion models for applications in text, image, or speech generation.
  • Cloud Platforms: Familiarity with cloud platforms (AWS, GCP, Azure), model deployment strategies, and GPU optimization.
  • Data Pipelines: Experience building data pipelines and data-centric applications using distributed storage platforms in a production setting.
  • Large-Scale Datasets: Ability to work with large-scale datasets, vector embeddings, and retrieval-based AI systems.

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