Enterprise AI Architect / AI Platform Architect

Acunor; San Diego (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Acunor is seeking a senior Enterprise AI Architect / AI Platform Architect to lead the design of enterprise AI platforms, AI-enabled SDLC frameworks, agentic AI solutions, and modernization strategies in a cloud-enabled environment.

This role requires a senior technology leader with experience across enterprise AI architecture, agentic AI, GenAI platforms, product engineering, APIs, microservices, cloud-native architecture, enterprise integration, and AI governance.

Qualifications

• 15+ years of experience in enterprise/ platform architecture, or technology transformation.
• Experience designing enterprise-scale AI, GenAI, agentic AI, or intelligent automation platforms.
• Strong understanding of LLMs, AI agents, RAG, enterprise search, orchestration, observability, MLOps/LLMOps, and AI governance.
• Experience with APIs, microservices, cloud-native platforms, and enterprise integration.
• Ability to influence executive stakeholders, architecture boards and engineering leaders.
• Exposure to LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, LlamaIndex, OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or Google Gemini preferred.

Responsibilities

• Define enterprise AI platform strategy, target-state architecture, and AI adoption roadmap.
• Architect scalable GenAI solutions, AI agents, RAG platforms, enterprise search, intelligent assistants, and automation frameworks.
• Design reusable AI platform capabilities including orchestration, evaluation, observability, governance, security, and lifecycle management.
• Design agentic AI architectures supporting autonomous workflows, contextual reasoning, tool usage, and human-in-the-loop execution.
• Establish patterns for AI-assisted engineering across requirements, design, development, testing, deployment, and documentation.
• Lead modernization using API-first, microservices, cloud-native, and event-driven architecture patterns.
• Create integration frameworks connecting legacy platforms, modern apps, AI services, data platforms, and workflows.
• Establish accelerators, platform standards, governance models, and enterprise architecture patterns.
• Act as trusted advisor to business, product, engineering, architecture, and executive stakeholders.
• Lead architecture workshops, discovery sessions, solution reviews, roadmaps, and technology strategy discussions.
• Translate business goals into practical AI platform roadmaps and executable architecture plans.

Skills

AI architecture

Platform architecture

GenAI platforms

APIs

Microservices

Cloud-native

Governance

LLMOps

LangChain

Executive influence

Tools

Cursor

Claude Code

GitHub Copilot

OpenAI

Anthropic

Azure OpenAI

AWS Bedrock

Google Gemini

LangChain

LangGraph

Semantic Kernel

CrewAI

AutoGen

LlamaIndex

Job description

Experience: 15+ years preferred

Role Overview

We are seeking a senior Enterprise AI Architect / AI Platform Architect to lead the design of enterprise AI platforms, AI-enabled SDLC frameworks, agentic AI solutions, and modernization strategies.

This role requires a senior technology leader with experience across Enterprise AI Architecture, Agentic AI, GenAI platforms, product engineering, application modernization, APIs, microservices, cloud-native architecture, enterprise integration, and AI governance.

Key Responsibilities

Enterprise AI Architecture & Platform Strategy

• Define enterprise AI platform strategy, target-state architecture, and AI adoption roadmap.
• Architect scalable GenAI solutions, AI agents, RAG platforms, enterprise search, intelligent assistants, and automation frameworks.
• Design reusable AI platform capabilities including orchestration, evaluation, observability, governance, security, and lifecycle management.

Agentic AI & AI-Enabled SDLC

• Design agentic AI architectures supporting autonomous workflows, contextual reasoning, tool usage, and human-in-the-loop execution.
• Establish architecture patterns for AI-assisted engineering across requirements, design, development, testing, deployment, and documentation.
• Guide responsible adoption of tools such as Cursor, Claude Code, GitHub Copilot, OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or Google Gemini.
• Lead modernization strategy using API-first, microservices-based, cloud-native, and event-driven architecture patterns.
• Design integration frameworks connecting legacy platforms, modern applications, AI services, data platforms, and enterprise workflows.
• Establish reusable accelerators, platform standards, governance models, and enterprise architecture patterns.

Leadership & Stakeholder Advisory

• Act as a trusted advisor to business, product, engineering, architecture, and executive stakeholders.
• Lead architecture workshops, discovery sessions, solution reviews, roadmap planning, and technology strategy discussions.
• Translate business goals into practical AI platform roadmaps and executable architecture plans.

Required Qualifications

• 15+ years of experience in enterprise architecture, AI architecture, platform architecture, product engineering, or technology transformation.
• Strong experience designing enterprise-scale AI, GenAI, agentic AI, or intelligent automation platforms.
• Deep understanding of LLM applications, AI agents, RAG, enterprise search, orchestration, observability, MLOps/LLMOps, and AI governance.
• Strong background in APIs, microservices, enterprise integration, cloud-native platforms, and application modernization.
• Experience defining architecture roadmaps, reusable frameworks, platform standards, and modernization accelerators.
• Ability to influence executive stakeholders, architecture boards, product teams, and engineering leaders.
• Exposure to LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, LlamaIndex, OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or Google Gemini is preferred.

Ideal Candidate Profile

The ideal candidate is a senior AI and platform architecture leader who can operate at both strategy and execution levels. This person should be able to define AI platform direction, guide architecture decisions, establish governance, influence stakeholders, and help engineering teams execute scalable AI adoption.