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GenAI Architect
2 months ago
Must Have Skills: AI/ML Architecture, NLP, Machine Learning, Deep Learning, LLM, GenAI Frameworks for LLM Development
Role Overview:
We are looking for a Generative AI Architect to join our team and help us to design, build, and deploy state of-the-art generative AI solutions. As a Generative AI Architect, you will be
Responsible for leading and mentoring a team of engineers in the development of innovative generative AI solutions to real-world problems
Work closely with pre-sales and business stakeholders to understand their needs and develop Solutions/PoCs that address those needs
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 using Python and related libraries
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:
o Design and implement document vectorization strategies to transform text documents into meaningful vector representations
o Develop and manage indexing techniques to support efficient retrieval of relevant information in a RAG setup
o Integrate vector databases and retrieval mechanisms to enhance the performance of RAG systems
o Optimize document indexing processes to ensure quick and accurate retrieval of contextually relevant information
Prompt management and evaluations using tools like LangFuse
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 team of data scientists and AI/ML engineers
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 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 related field
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, RAG and cloud based AI solutions
Knowledge of AI Testing tools
Technical Skills:
Expertise in generative AI models, including transformers, GANs, and VAEs
Proficiency in LangChain, LangSmith for integrating and managing language models
Exposure to major LLMs, HuggingFace, Kaggle 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 management of cloud-based AI infrastructures
High level understanding of containerization and orchestration technologies (e.g., Docker, Kubernetes) is a plus