Senior AI Architect - Enterprise GenAI & Agentic Platforms

Accenture; Culver City (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Accenture is seeking an AI/ML Senior Architect to design and scale enterprise-grade Agentic AI platforms across public, private, or sovereign clouds. You will partner with clients to set AI strategy, architect reference solutions, and deliver AI agent frameworks leveraging GenAI and foundation models.

Responsibilities include end-to-end architecture, governance, security, observability, and driving MLOps pipelines across the lifecycle of multiple AI solutions. Travel 25%–100% may be required.

Qualifications

• 12+ years of experience with Python, Java, or equivalent.
• 6+ years of experience with AI technologies from public cloud providers, open source, and tooling.
• 4+ years designing and operationalizing large-scale AI/ML solutions on at least one major public cloud.

Responsibilities

• Architect enterprise-ready AI agent systems including retrieval, orchestration, governance, and observability.
• Lead architecture workshops with client and Accenture teams; define requirements and TCO for AI solutions.
• Evaluate and select technologies for full-stack AI platforms; build financial viability models.
• Design architectural blueprints for multi-agent systems, security, governance, and knowledge layers.
• Drive AgentOps/LLMOps practices across the ML/LLM lifecycle and enable scalable knowledge architectures.

Skills

Python

Java

Observability

AI platforms

Agent orchestration

Education

Bachelor's degree or equivalent

Tools

Claude

LangGraph

CrewAI

AutoGen

Job description

Accenture is seeking an AI/ML Senior Architect to design and scale enterprise-grade Agentic AI platforms across public, private, or sovereign clouds. You will partner with clients to set AI strategy, architect reference solutions, and deliver AI agent frameworks leveraging GenAI and foundation models.

Responsibilities include end-to-end architecture, governance, security, observability, and driving MLOps pipelines across the lifecycle of multiple AI solutions. Travel 25%–100% may be required.