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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence Engineer - **Company:** GAC Solutions Inc. - **Location:** Falls Church, VA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Automation of Tests, Microsoft Azure, Cloud Engineering, Code Review, Computer Programming, Continuous Integration, DevOps, Software Architecture, Search Technologies, Software Deployment, Software Engineering, Systems Integration, Enterprise Software Applications, Spring Cloud, GitHub Copilot, Large Language Models, Multi-Agent Systems, Git, Event Driven Architecture, Performance Monitor, Enterprise Integration, Virtual Agents, Restful APIs, Microservices - **Published:** September 27, 2026 - **Apply:** https://www.disabledperson.com/jobs/75535678-artificial-intelligence-engineer ## About the Role The ideal candidate is an experienced software engineer who remains actively involved in coding and uses GitHub Copilot and/or Claude Code CLI as part of their day-to-day development workflow. This role requires proven experience taking agentic AI or LLM-based solutions beyond proof-of-concept into production environments, with strong foundations in cloud-native engineering, Microservices, APIs, and DevOps., * 7+ years of experience in hands-on software engineering, including Microservices, APIs, cloud-native applications, and enterprise software development. * 3+ years of strong hands-on experience with AWS, DevOps/CI-CD, and cloud-native architecture. * Proven hands-on experience personally building and deploying an AI agent, LLM integration, or agentic workflow into a production environment. * Current hands-on development experience, with active individual-contributor coding experience within the last 6 months. * Demonstrated day-to-day use of GitHub Copilot and/or Claude Code CLI for software development, with the ability to explain specific real-world use cases. Technical Skills * Agentic AI: AI Agents, Agentic Workflows, Tool Calling, Multi-Agent Systems * Agent Frameworks: LangGraph, LangChain, CrewAI, AutoGen, Strands, Bedrock Agents * Protocols: Model Context Protocol (MCP) * AI/LLM Platforms: AWS Bedrock, Anthropic APIs, OpenAI APIs, Azure AI Foundry * Cloud: AWS * Architecture: Microservices, REST APIs, Event-Driven Architecture, Cloud-Native Applications * AI Coding Tools: GitHub Copilot, Claude Code CLI * DevOps: Git, CI/CD, automated testing and deployment * LLM Observability: Langfuse, LangSmith, Braintrust, Weights & Biases * AI Patterns: RAG, Embeddings, Vector Search, LLM Evaluation, Guardrails Strongly Preferred * Hands-on experience developing an MCP server or MCP-enabled enterprise integration. * Production experience with LangGraph, LangChain, CrewAI, AutoGen, Strands, or Bedrock Agents. * Strong production experience with AWS Bedrock. * Experience building internal developer platforms or engineering productivity products. * Experience implementing production RAG systems with measurable retrieval quality and evaluation frameworks. * Experience implementing LLM observability and evaluation using Langfuse, LangSmith, Braintrust, or W&B. * Experience with AI governance and Responsible AI controls, including guardrails, prompt-injection protection, access controls, and audit logging. * Experience designing secure enterprise integrations between AI agents and internal systems. * Strong software architecture, troubleshooting, communication, and problem-solving skills. ## Description * Build and maintain Microservices, REST APIs, event-driven services, and enterprise integrations. * Use GitHub Copilot and/or Claude Code CLI as part of daily software development and engineering activities. * Design and implement agent workflows using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Strands, or Bedrock Agents. * Build integrations using Model Context Protocol (MCP) and develop MCP servers where applicable. * Develop internal developer platforms, engineering productivity tools, and reusable AI capabilities. * Design and implement production-grade RAG solutions, including retrieval, relevance evaluation, and quality optimization. * Integrate enterprise applications with AWS Bedrock, OpenAI, Anthropic, or other LLM APIs. * Implement LLM observability, tracing, evaluation, and performance monitoring. * Develop AI guardrails, audit logging, security controls, and prompt-injection defenses. * Build automated testing and evaluation processes for AI/LLM applications. * Implement CI/CD pipelines and support production deployment, monitoring, troubleshooting, and continuous improvement. * Participate in architecture discussions and code reviews while remaining a strong hands-on individual contributor. ## Related Videos - [AI Killed DevOps... 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