Find & Apply For Artificial Intelligence Engineer Jobs in California City, California

Role Name:

AI Application Architect Location:

California City, CA ( Onsite )

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GC Job Type:

Full Time Experience:

16+

Years Interview Mode:

Virtual Pay Rate:

Based on

Experience Job Description:

We are seeking a highly experienced AI Application Architect to architect, design, and guide the development of enterprise-grade AI agent applications and intelligent enterprise solutions. The ideal candidate will have 16+ years of software/application architecture experience with strong hands-on expertise in AI/LLM technologies, agentic frameworks, Python, AWS, distributed systems, and cloud-native architectures . The AI Application Architect will be responsible for defining technical architecture, integration patterns, security standards, scalability strategies, and implementation approaches for production-ready AI agents and enterprise tool integrations.

Key Responsibilities:

Define and lead the architecture and technical design of enterprise-grade AI agent applications. Collaborate with business, product, engineering, security, and cloud teams to define AI solution requirements, architecture, integration patterns, and implementation strategies. Design orchestration agents and domain-specific agents using Python 3.12, LangChain, LangGraph, or Strands . Architect solutions integrating LLMs and foundation models for reasoning, structured responses, tool calling, and workflow execution. Define architecture and deployment strategies for AI agents using Amazon Bedrock AgentCore Runtime . Architect agent-to-agent communication using A2A 1.0 and the official Python a2a-sdk . Design secure agent-to-tool integrations using Model Context Protocol (MCP) and Amazon Bedrock AgentCore Gateway . Define data architecture using PostgreSQL for task persistence, workflow status, agent state, and idempotency management. Architect secure document and artifact management using Amazon S3 and credential management through AWS Secrets Manager . Define observability architecture using AgentCore Observability, OpenTelemetry, and Amazon CloudWatch . Establish standards for logging, metrics, distributed tracing, monitoring, security, scalability, and reliability of AI applications. Define testing strategies covering unit, integration, A2A contract, performance, and end-to-end testing using tools such as pytest. Evaluate emerging AI technologies, agentic frameworks, protocols, and cloud services for enterprise adoption. Establish architecture standards, reusable patterns, technical guidelines, and best practices for AI application development. Provide technical leadership and mentorship to AI engineers and development teams. Review architecture, source code, integration designs, and implementation approaches to ensure alignment with enterprise standards. Troubleshoot complex architectural and production issues involving AI applications, distributed systems, APIs, cloud infrastructure, and integrations.

Qualifications:

Experience:

16+ years of experience in software engineering, application architecture, solution architecture, or related technology roles, with significant experience designing enterprise applications.

AI & LLM

Architecture:

Proven experience architecting AI applications and integrating LLMs/foundation models with enterprise systems.

Python Engineering:

Strong proficiency in Python 3.12+ , asynchronous programming, API development, and object-oriented design.

Agentic AI:

Hands-on architecture experience with LangChain, LangGraph, Strands , or similar agentic AI frameworks.

Prompt Engineering:

Strong understanding of prompt engineering, tool calling, structured outputs, context management, and LLM response validation .

Cloud & APIs:

Strong experience with AWS services, REST APIs, relational databases, and cloud-native application architecture .

Architecture:

Strong understanding of multi-agent orchestration, distributed systems, event-driven architecture, task persistence, asynchronous workflows, scalability, and high availability .

Enterprise Integration:

Experience designing integrations between AI agents, enterprise applications, APIs, databases, and external tools.

Security:

Strong understanding of secure AI application architecture, authentication, authorization, secrets management, data protection, and enterprise security standards.

Leadership:

Strong technical leadership, architecture governance, problem-solving, communication, and cross-functional collaboration skills.

Education:

Bachelor s or Master s degree in Computer Science, Engineering, or a related field.

Preferred Skills:

AWS Bedrock Stack:

Experience architecting solutions using Amazon Bedrock, AgentCore Runtime, AgentCore Gateway, and AgentCore Observability .

Protocols & MCP:

Strong knowledge of A2A protocols, MCP clients and servers , and enterprise tool integration.

Infrastructure & Tools:

Experience with PostgreSQL, Amazon S3, AWS Secrets Manager, OpenTelemetry, CloudWatch, HashiCorp, and pytest .

Production Architecture:

Experience architecting secure, scalable, highly available, and production-grade AI applications.

DevOps & MLOps:

Experience with Docker, Kubernetes, CI/CD pipelines, RAG, LLM evaluation, AI guardrails , and cloud-native deployment. Experience designing multi-agent systems and agent orchestration architectures . Experience with AI governance, responsible AI, observability, model evaluation, and enterprise AI security . Experience working with large-scale distributed systems and high-volume enterprise applications .