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GenAI Architect

2 months ago

bangalore, India Creative Synergies Group Full time

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