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# AI Solution Architect

- **Company:** Net2Source
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Delivery, Continuous Integration, Information Engineering, Data Integration, Software Design Documents, Python (Programming Language), Unstructured Data, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Terraform, Oracle Cloud Infrastructure, Automation Anywhere
- **Published:** November 9, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-solution-architect-san-francisco-ca--b36444ef-a854-4caf-b384-dc4e5cd346cb

## About the Role

Artificial Intelligence (AI), Artificial Intelligence (AI), Unstructured Data, Automation, Structured Data, Maintain Compliance, Testing, Continuous Deployment/Delivery, Theater Production, Continuous Integration, * 8+ years in solution architecture; 3+ years in AI/LLM systems.

 * Expertise in cloud platforms (Azure/AWS/GCP), Python, and AI frameworks (e.g., LangChain/LangGraph, vector databases).
 * Strong knowledge of data engineering, security, governance, CI/CD, IaC (e.g., Terraform), and runtime observability (metrics, logs, tracing).
 * Oracle OCI experience is a plus.

Preferred

 * Experience with multi-agent orchestration, enterprise integrations (ticketing, reporting, messaging), and regulated environments.
 * Familiarity with governance processes and mixed DB landscapes.

## Description

* Define reference architectures for LLM/RAG and multi-agent workflows.
 * Develop solution architectures for structured and unstructured data using RAG; create Action Agents for task automation.
 * Integrate heterogeneous data sources securely; ensure compliance and auditability.
 * Build production-ready solutions with CI/CD, IaC, observability, rollbacks, and SLOs; lead deployment across environments (dev/test/staging/prod).
 * Quickly learn AT&T tools, frameworks, processes, and landscape; analyze use cases and identify optimal solutions.
 * Apply best practices and avoid anti-patterns in architecture, data integration, and AI workflows.
 * Create solution design documents for each use case and develop proofs of concept (POCs) to validate feasibility.
 * Partner with stakeholders to prioritize use cases and mentor engineering teams.