AI Product Architect

Designing Enterprise AI Solutions

Shape how Generative AI, large language models, and AI agents integrate with enterprise systems and data. Work closely with business stakeholders and technical teams to guide implementation, translating complex business requirements into secure, scalable architectures.

Key Responsibilities

• Design end-to-end AI solution architectures covering Generative AI, LLMs, and agentic workflows.
• Translate business processes and requirements into clearly defined AI capabilities and technical designs.
• Design RAG solutions across structured and unstructured enterprise data.
• Define integration approaches between AI services, enterprise platforms, and legacy systems.
• Embed security, privacy, responsible AI, and governance into solution designs.
• Produce conceptual, logical, and technical architecture documentation, including sequence flows and integration patterns.
• Communicate architectural decisions to technical and non-technical stakeholders and support engineering teams throughout delivery.

Required Experience & Skills

• Strong experience as an AI Architect, Solution Architect, Data/AI Architect, or Enterprise Architect delivering complex enterprise solutions.
• Hands-on knowledge of Generative AI, LLM, and agentic AI architectures.
• Strong understanding of RAG, embeddings, vector and hybrid search, reranking, chunking, and grounding.
• Experience with cloud AI platforms, ideally AWS and/or Microsoft Azure.
• Understanding of agent orchestration and frameworks such as LangGraph, Strands, or CrewAI.
• Strong knowledge of APIs, event-driven architecture, microservices, and enterprise integration patterns.
• Experience designing solutions using structured and unstructured data.
• Understanding of AI security, responsible AI, privacy, and governance.
• Strong architecture documentation, communication, and stakeholder-management skills.

Desirable Experience

• Amazon Bedrock, Amazon Bedrock AgentCore, Azure AI services, or comparable enterprise AI platforms.
• Snowflake and modern AI/data architectures.
• Model Context Protocol (MCP) and emerging agent interoperability standards.
• Integration with platforms such as ServiceNow, SAP, Salesforce, or Workday.
• AI evaluation, LLMOps/MLOps, prompt management, and model/agent observability.
• Designing multi-agent systems and controlled autonomous workflows.