Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role.

Responsibilities:

Your technical responsibilities:

• Contribute to the design and implementation of state-of-the-art AI solutions.
• Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.
• Collaborate with stakeholders to identify business opportunities and define AI project goals.
• Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
• Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases.
• Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
• Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
• Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.
• Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency.
• Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases.
• Ensure compliance with data privacy, security, and ethical considerations in AI applications.
• Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications.