Artificial Intelligence Model Architect

1 day ago

Nagpur, Maharashtra, India beBeeGenerative Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Advanced AI Model Developer

The Advanced AI Model Developer plays a pivotal role in driving innovation by designing and implementing cutting-edge generative AI models to meet the evolving needs of clients. Key responsibilities include fine-tuning large language models, optimizing scalable machine learning pipelines, and integrating AI solutions into client applications for seamless performance.

Key Responsibilities:
  • Develop Next-Generation Generative AI Models: Conduct research, design, and fine-tune state-of-the-art models like LLaMA, GPT, Stable Diffusion, and 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: Collaborate closely with business and engineering teams to embed generative AI solutions into real-world applications, ensuring seamless user experiences.
  • Optimize for Scale & Performance: Develop and refine scalable ML 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.

Required Skills and Qualifications:

  • 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 and 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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