AI Innovation Leader
13 hours ago
As a seasoned AI Innovator, you will play a pivotal role in driving innovation by designing and implementing cutting-edge generative AI models to meet client needs. Key responsibilities include fine-tuning Large Language Models, optimizing scalable Machine Learning pipelines, and integrating AI into client applications for seamless performance.
Key Responsibilities:
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- Develop Next-Generation Generative AI Models: Research, design, and fine-tune state-of-the-art models like Llama, GPT, Stable Diffusion, and Variational Autoencoders 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 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.\
- Communicate complex AI concepts to technical and non-technical audiences, ensuring alignment with business strategies.
To excel in this role, you should have the following skills and qualifications:
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- 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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