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Job Overview:
As a Generative AI Architect at Xebia, you will play a pivotal role in designing and implementing advanced generative AI solutions. Your expertise will drive innovation in our AI initiatives focusing on document vectorization, indexing for Retrieval-Augmented Generation (RAG), and other cutting-edge technologies, ensuring the development of scalable and effective generative models. You will leverage your knowledge in LangChain, LangSmith, prompt engineering, and cloud technologies to build state-of-the-art AI systems.
Key Responsibilities:
Architectural Design:
Design and develop scalable generative AI systems and frameworks.
Evaluate and select appropriate generative models and algorithms for various use cases.
Ensure architectural decisions align with organizational goals and technical requirements.
Design and integrate end-to-end use cases as per the requirements.
Generative Model Understanding:
Implement and optimize generative models, including but not limited to transformers, Open AI.
Develop and refine techniques for improving model performance, such as fine-tuning and transfer learning.
LangChain, LangSmith Integration:
Utilize LangChain, LangSmith to build and deploy complex AI systems that integrate multiple language models.
Design workflows and pipelines using LangChain, LangSmith for effective prompt management and model orchestration.
Prompt Engineering and Methods:
Develop and implement prompt engineering strategies to enhance model outputs.
Experiment with and optimize various prompt methods to address specific application needs.
Collaborate with stakeholders to create and refine prompts for different scenarios.
Document Vectorization and Indexing for RAG:
Design and implement document vectorization strategies to transform text documents into meaningful vector representations.
Develop and manage indexing techniques to support efficient retrieval of relevant information in a RAG setup.
Integrate vector databases and retrieval mechanisms to enhance the performance of RAG systems.
Optimize document indexing processes to ensure quick and accurate retrieval of contextually relevant information.
Technical Implementation:
Write and maintain Python code for model training, evaluation, and deployment.
Implement best practices for code quality, version control, and testing.
Cloud and Infrastructure:
Design and manage cloud-based AI solutions using platforms such as AWS, Azure, or Google Cloud.
Ensure the scalability and reliability of AI systems in cloud environments.
Optimize resource usage and manage costs associated with cloud infrastructure.
Collaboration and Leadership:
Lead and mentor a team of data scientists, engineers, and researchers.
Collaborate with cross-functional teams to define project requirements and deliver solutions.
Communicate technical concepts and results to non-technical stakeholders.
Research and Development:
Stay updated with the latest advancements in generative AI and related technologies.
Contribute to research publications and participate in relevant conferences or forums.
Qualifications:
Education:
Bachelor's or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
Ph.D. in a relevant field is a plus.
Experience:
- Proven experience as an AI Architect or in a similar role, focusing on generative AI.
- Strong portfolio of projects involving LangChain, LangSmith, prompt engineering, and cloud-based AI solutions.
Technical Skills:
- Expertise in generative AI models, including transformers, GANs, and VAEs.
- Proficiency in LangChain, LangSmith for integrating and managing language models.
- Advanced knowledge of prompt engineering techniques and prompt methods.
- Strong programming skills in Python, with experience in relevant libraries (e.g., TensorFlow, PyTorch).
- Experience with cloud platforms (AWS, Azure, Google Cloud) and managing cloud-based AI infrastructures.
- Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes) is a plus.
Soft Skills:
- Excellent problem-solving and analytical skills.
- Strong leadership and team management capabilities.
- Effective communication skills, with the ability to present complex concepts clearly.
- Ability to work collaboratively in a fast-paced, dynamic environment.
