GenAI Innovator

1 day ago

Vellore, Tamil Nadu, India beBeeGenerative Full time ₹ 1,50,00,000 - ₹ 2,20,00,000

Embark on a cutting-edge career as a GenAI Innovator, where you'll push the boundaries of artificial intelligence to drive business impact.

About the Role
  • Design and implement advanced generative AI models to meet client needs, leveraging state-of-the-art technologies like Llama, GPT, Stable Diffusion, and VAEs.
Key Responsibilities
  • Develop next-generation generative AI models by researching, designing, and fine-tuning these models to optimize performance and efficiency.
  • Enhance AI capabilities by implementing and optimizing LLM fine-tuning, prompt engineering, and AI safety mechanisms to improve model usability.
  • Integrate AI into products/services by working closely with business and engineers to embed generative AI solutions into real-world applications, ensuring seamless user experiences.
  • Optimize for scale and performance by building and refining scalable ML pipelines for efficient model deployment, inference, and real-time adaptation.
  • Stay ahead of AI trends by keeping pace with emerging technologies, model architectures, and industry breakthroughs to drive continuous innovation.
  • Communicate complex AI concepts effectively to technical and non-technical audiences, ensuring alignment with business strategies.
Requirements
  • 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).
Essential Skills
  • At least 4-5 years of experience in Data Science domain with strong exposure to Generative AI.
  • Experience using Machine and Deep Learning languages, preferably Python and R, to manipulate data and draw insights from large datasets.
  • Hands-on experience with LLMs, multimodal AI, and diffusion models for applications in text, image, or speech generation.
  • Strong proficiency in TensorFlow, PyTorch, Hugging Face Transformers, and OpenAI APIs, along with custom fine-tuning of LLMs and AIops toolkits.
  • Familiarity with cloud platforms (AWS, GCP, Azure), model deployment strategies, and GPU optimization.
  • Experience building data pipelines and data-centric applications using distributed storage platforms in a production setting.
  • Ability to work with large-scale datasets, vector embeddings, and retrieval-based AI systems.

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