Genai Architect

2 months ago

Delhi, India Creative Synergies Group Full time
Must Have Skills: AI/ML Architecture, NLP, Machine Learning, Deep Learning, LLM, GenAI Frameworks for LLM DevelopmentRole 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 systemsKey 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 requirementsGenerative 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 orchestrationPrompt 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 representationso Develop and manage indexing techniques to support efficient retrieval of relevant information in a RAG setupo Integrate vector databases and retrieval mechanisms to enhance the performance of RAG systemso Optimize document indexing processes to ensure quick and accurate retrieval of contextually relevant information Prompt management and evaluations using tools like LangFuseTechnical 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 infrastructureCollaboration 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 stakeholdersResearch and Development: Stay updated with latest advancements in generative AI and related technologies Contribute to research publications and participate in relevant conferences or forumsQualifications: Education:Bachelor's or Master’s degree in Computer Science, Data Science, Machine Learning, or related fieldExperience: 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 toolsTechnical 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

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