Location - Santa Clara, CA (On-site)

Contractr/FTE

Rate/Salary :: Depends on Expereince

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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 year's 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 developer's 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