Engineering Architect- GEN AI
1 week ago
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
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