Engineering Architect- GEN AI

GenAI Engineering Architect (Must have - AutoGen, CrewAI and WrenAI) 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, CrewAI, 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, CrewAI, WrenAI, 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 databases 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 + AI project architectural leadership of at least 3 years • 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 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