| GenAI Engineering Architect (with Autogen) || Location - Santa Clara, CA at Remote, Remote, USA |
| Email: [email protected] |
| GenAI Engineering Architect (with Autogen) This role requires hands-on project expertise to implement an enterprise application built on top of SQL and unstructured data (images,videos,logs etc.) using Autogen, Azure OpenAI GPT-4 Turbo and GPT-4V with PTUs. This is a hands-on architect role requiring both deep technical skills and the ability to deliver complex AI applications end-to-end on large operational databases to render charts, tables and other insights as completions from NLP-based prompts. Deep experience of Autogen and Azure AI Search is a MUST. This is not a document retrieval, summarization or semantic search-based role. Responsibilities: Architectural Design: Collaborate with stakeholders to understand business requirements and translate them into architectural blueprints. Design scalable, secure, and high-performance architecture for the Autogen-based LLM-Integrated application. Define data models and schemas for integrating operational data from relational databases into the application. Implementation and Development: Lead the implementation efforts, ensuring adherence to architectural guidelines and best practices. Develop robust APIs and interfaces for seamless communication between the application and relational databases. Write efficient and maintainable code, following coding standards and version control processes. Integration and Testing: Integrate operational data from various relational databases into the application, ensuring data consistency and integrity. Conduct thorough testing, including unit testing, integration testing, and performance testing, to validate the functionality and scalability of the application. Troubleshoot and debug issues as they arise during the integration and testing phases. Optimization and Performance Tuning: Identify performance bottlenecks and optimization opportunities within the application architecture. Implement performance tuning strategies to improve the speed, reliability, and efficiency of data retrieval and processing. Continuously monitor system performance and proactively address any degradation or inefficiencies. Documentation and Knowledge Sharing: Create comprehensive technical documentation, including architecture diagrams, API specifications, and deployment procedures. Conduct knowledge sharing sessions to disseminate architectural knowledge and best practices among team members. Provide guidance and mentorship to junior team members, fostering their professional growth and development. Requirements: Must have : Autogen Framework, SQl Agents, AG-Grid Flask / Django / Fast API development expertise with least 2-3 project delivered as a lead developer / implementation architect. Must have : Core Python Iterators, Generators , OOP concepts, Python Shell (REPL) and Object Relational Mapper, Data structure and Exception handling etc. Must have : AI Search,Vector Database creation for relational databses and unstructured data Must have : Azure app services expertise in terms of building and deploying AI apps using cloud services. Must have : Deep expertise in Azure SQL, Azure Data Factory , Linked Services and Azure Synapse etc. 9-10 years of overall technology experience in core application development 5+ years experience leading development of AI application using Python backend frameworks and multiple inferencing pipelines Rapid PoC/Prototyping skills and expertise in building and demonstrating application blueprints without need a developers assistance. Deep, hands-on and architectural proficiency in Python,Ag-Grid and ReactJS Hands-on expertise of SharePoint indexes and data/file structures (Azure SQL) Good knowledge of Azure Form Recognizer for OCR of complex images, forms and other data Handson with implementing TaskWeaver, Autogen, Agentic Flows, Retrieval Augmented Generation (RAG) and RLHF (Reinforcement Learning from Human Feedback) Designing and implementing vector databases on Azure cloud using Ai Search and Cosmos DB vCore Sound project implementation level knowledge of Pinecone,FAISS,Weaviate or ChromaDB Deep expertise in Prompt Engineering using DsPy tools etc. Knowledge of NLP techniques like transformer networks, embeddings, intent recognition etc. Hands-on skills on Embedding and finetuning Azure OpenAI using MLOPS/LLMOPS pipelines. Strong communication, architectural sketching, and collaboration skills Regards, Disha Gupta -- Keywords: artificial intelligence database information technology GenAI Engineering Architect (with Autogen) || Location - Santa Clara, CA [email protected] |
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| Sat Jun 08 02:51:00 UTC 2024 |