AI Architect (Agentic AI, Only local to TX) – C2C Jobs

AI Architect with Agentic AI Experience

Austin, TX (Hybrid)

Contract

Rate: $75-$80/hr on C2C (Max)

Principal Architect with deep AI and agentic systems expertise to develop and manage highly critical and high-volume applications. Modernization from legacy systems is preferred. This role requires a balance of architecture leadership and hands-on engineering, with a strong focus on LLM orchestration, memory management, advanced retrieval (RAG), and enterprise-grade agent workflows.
The ideal candidate has proven experience building scalable agentic systems in enterprise environments, not just experimental or IDE-based solutions, and can design systems that integrate business context, code intelligence, and human-in-the-loop workflows
 

Must-Have Skills

Recruiters should screen strongly for the following:

1) Agentic AI / LLM Systems

Proven experience building agentic systems that run-in server environments
Experience with multi-step orchestration, tool calling, and workflow design
Hands-on implementation experience — not just conceptual AI knowledge
2) Memory and Context Management

Experience designing short-term and long-term memory for AI systems
Understanding of how to manage context across sessions, tasks, and workflows
Familiarity with state handling, summarization, persistence, and context optimization
3) Advanced Retrieval / RAG

Strong experience with advanced RAG architectures
Hands-on work with:
Vector databases
Graph databases
Embeddings and retrieval design
Experience with hybrid retrieval patterns and building richer context for AI systems
4) Code Intelligence / Technical Analysis

Experience ingesting source code and documents into AI/retrieval workflows
Ability to support:
impact analysis
dependency mapping
technical analysis
code relationship/call graph understanding
Experience with AST parsing using tools such as Tree-sitter or similar
5) Programming / Engineering Depth

Strong hands-on experience in:
Java (Python add on)
Agentic Platforms, RAG/GraphDB, LLMs
Caching – near and distributed (Redis ,Apache Ignite etc)
API Token management optimization techniques is a plus
Experience with SOLR, Lucene or equivalent is a plus
Google Cloud Experience is a plus
Enough engineering depth to contribute meaningfully to implementation and technical design
6) Frameworks / Platforms

Experience with one or more of the following is important:

LangGraph
LangChain
GitHub Copilot SDK or similar
CLI-based/server-hosted execution models
Managed cloud AI platforms such as Bedrock or similar offerings