Lead AI Architect: Agentic Systems & LLM Orchestration

Accenture PLC; Chantilly (VA); On-site; USD 130,000 - 265,000; Full time

Job summary

Accenture Federal Services is seeking a senior architect to lead the end-to-end design, development, and deployment of an agentic AI system that orchestrates user queries across heterogeneous data sources. Must have experience designing and deploying LLM-based systems, including RAG, tool-calling, and multi-agent patterns, and a strong understanding of embeddings, vector search, and orchestration layers.

The architect defines the overall system architecture, selects AI/ML and platform

Qualifications

• 8 years of experience with AI systems and agentic systems.
• 4 years experience with AI and/or Data.
• Experience with Python, agent frameworks, data engineering, APIs/microservices, vector databases, SQL engines, distributed systems, cloud services, RAG.
• Understanding of DevOps tools and principals.

Responsibilities

• Lead the end-to-end technical design, development, and implementation of an agentic AI system to orchestrate user queries across heterogeneous data sources.
• Define the overall system architecture, selecting and governing AI/ML and platform technologies, ensuring scalability, security, and production-readiness.
• Partner closely with development, DevOps and data engineering teams to translate project requirements into an extensible AI architecture.
• Ensure traceability, reliability, and production readiness of the solution.

Skills

AI systems

Agentic systems

Distributed systems

Cloud services

Data engineering

APIs

Vector databases

SQL engines

RAG

DevOps

Education

Bachelor's degree

Model Context Protocol

Tools

Python

Agent frameworks

APIs/microservices

Vector databases

SQL engines

Distributed systems

Cloud services

RAG

Job description

Accenture Federal Services is seeking a senior architect to lead the end-to-end design, development, and deployment of an agentic AI system that orchestrates user queries across heterogeneous data sources. Must have experience designing and deploying LLM-based systems, including RAG, tool-calling, and multi-agent patterns, and a strong understanding of embeddings, vector search, and orchestration layers.

The architect defines the overall system architecture, selects AI/ML and platform