Azure Architect - Cloud Architecture

We Are PepsiCo
Join PepsiCo and Dare for Better! Being part of PepsiCo means being part of one of the largest food and beverage companies in the world, with our iconic brands consumed more than a billion times a day in more than 200 countries.
Our product portfolio, which includes 22 of the world's most iconic brands, such as Sabritas, Gamesa, Quaker, Pepsi, Gatorade and Sonrics, has been a part of Mexican homes for more than 116 years.
The AI/ML Architect is responsible for designing, developing, and implementing advanced AI-driven conversational and agentic solutions using Azure, AWS or custom developed AI services, cutting‑edge language models, and modern programming languages such as Python and Java. This role demands deep expertise in natural language processing (NLP), dialog management, agent orchestration, and cloud‑native AI deployments to create seamless, intelligent, and scalable conversational experiences and autonomous agents.
Conversational & Agentic AI Solution Architecture & Design:
Design and architect end‑to‑end conversational AI systems, including chatbots, voice assistants, and intelligent agents, leveraging advanced NLP techniques and large language models (LLMs).
Develop comprehensive dialog flows, intent recognition models, entity extraction, and sentiment analysis for complex conversational interactions.
Architect agentic systems, focusing on planning, reasoning, and execution, integrating tools and APIs for autonomous task completion.
Define the architecture for integrating conversational and agentic AI with enterprise systems and external APIs.
Cloud & Infrastructure Development (Focused on Conversational & Agentic AI):
Design and deploy multiple AI agents using open source development, deployment and orchestration frameworks like LangGraph and Crew.ai or their commercial equivalent.
Optimize cloud resources for efficient processing of natural language queries, LLM inferences and agentic workflows.
Implement serverless architectures and containerized solutions (Docker, Kubernetes) for deploying and managing conversational and agentic AI models.
Database & Knowledge Engineering (Focused on Conversational & Agentic AI):
Design and implement knowledge graphs and vector databases (e.g., Azure Cognitive Search, Pinecone, Weaviate) to support conversational and agentic AI systems.
Develop data pipelines for ingesting, processing and storing conversational data, including transcripts, user interactions and knowledge base content.
Implement data retrieval and augmentation strategies, including Retrieval Augmented Generation (RAG) to enhance the accuracy and relevance of conversational and agentic responses.
Integration & Deployment (Focused on Conversational & Agentic AI):
Integrate conversational and agentic AI solutions with various channels, including web, mobile, messaging platforms (SMS, WhatsApp, etc.), Social Media (Facebook Messenger, Ticktock, etc.) Performance Optimization & Governance (Focused on Conversational & Agentic AI):
Monitor and optimize the performance of conversational and agentic AI systems, including response latency, accuracy and user satisfaction.
Implement robust testing and evaluation frameworks for conversational and agentic AI models, including A/B testing and user feedback analysis.
Collaborate with product managers, data scientists and engineers to define and deliver conversational and agentic AI solutions.
Provide technical guidance and mentorship to junior team members on conversational and agentic AI development.
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Bachelor’s or Master’s degree in Computer Science, AI, Natural Language Processing, or a related field.