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# AI Engineer

- **Company:** System One
- **Location:** Atlanta, GA, United States (Remote available)
- **Salary:** $155,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Integration Architecture, Application Services, JIRA, Authentication Protocols, Automation of Tests, Microsoft Azure, Cloud Computing, Cloud Engineering, Code Review, Collaborative Software, Databases, Continuous Integration, Information Engineering, Web Development, Github, Design of User Interfaces, Issue Tracking Systems, JSON, Python (Programming Language), Machine Learning, Open Source Technology, OpenAI, Cloud Services, Software Engineering, Web Application Frameworks, Microsoft Agent Framework, Cloud Platform System, Chatbots, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Backend, Agentic-AI, Git, Information Technology, Low Latency, PydanticAI Framework, Machine Learning Operations, Virtual Agents, Evaluation of Large Language Models, Invoking Functions, Api Design, Restful APIs, Model Context Protocol, Data Pipelines, Devsecops, Docker, Container Apps
- **Published:** November 5, 2026
- **Apply:** https://www.juju.com/job/1_systemoneservices-5c262d37

## About the Role

+ 5+ years of hands-on technical experience in software engineering, web application development, data engineering, data science, machine learning, or related technical discipline
 + 1+ years of hands-on experience building generative AI or agentic AI solutions, including LLM APIs, prompt engineering, tool/function calling, structured outputs, retrieval-augmented generation (RAG), agents, or similar techniques
 + Experience developing applications with Python and working with APIs, backend services, data pipelines, and web application frameworks
 + Experience with REST APIs, JSON-based interfaces, authentication mechanisms, and external service integrations
 + Experience with at least one major cloud platform, such as Microsoft Azure or AWS, including deploying or integrating applications with cloud-hosted services
 + Using modern software-development practices and collaboration tools, such as Git, GitHub, Jira, pull requests, code reviews, issue tracking, and Agile/Scrum delivery practices
 + Knowledge of LLM application fundamentals, including tokens, context windows, system and user prompts, model parameters, structured outputs, embeddings, retrieval, and techniques for guiding and evaluating model responses
 + Ability to obtain and maintain a Public Trust or suitability determination, as required by the client or contract
 + Bachelor's degree in computer science, engineering, or a related field
 + Must be a U.S. Citizen or Lawful Permanent Resident (Green Card Holder)
Desired Skills:

 + Experience with containerized or cloud-native application development, including Docker, Azure Container Apps, Azure App Service, or similar services
 + Experience with Model Context Protocol (MCP) integrations and agent tool calling, including hands-on development of multi-agent systems
 + Experience with implementing LLM evaluation, tracing, monitoring, and observability capabilities
 + Experience with CI/CD, automated testing, and cloud-based development and deployment practices for AI applications
 + Working in government, healthcare, or other regulated environments
 + Exposure to fine-tuning, hosting, or serving open-source LLMs and working with model-serving frameworks
 + Knowledge of AI governance, responsible AI principles, and related security and risk-management practices
 + Knowledge of AI agent frameworks such as Microsoft Agent Framework, LangGraph, PydanticAI, or similar tools
 + Ability to work effectively in a cross-functional engineering environment and collaborate with software, AI/ML, data, cloud, and DevSecOps engineers
 + Clear communication of technical problems, implementation decisions, and trade-offs to both technical and non-technical stakeholders

## Description

+ Build and enhance production generative AI applications and web-based AI solutions.
 + Develop AI applications using Azure AI Foundry, Azure OpenAI, and Foundry Agent Service.
 + Build retrieval-augmented generation (RAG) systems, knowledge assistants, chatbots, and agentic workflows.
 + Implement agent and LLM workflows that use tools, structured outputs, guardrails, and human review where needed.
 + Integrate AI applications with APIs, enterprise data sources, databases, cloud services, and MCP-based tools.
 + Develop secure, cloud-native application services and user interfaces using Python, web frameworks, containers, and Azure services.
 + Test, evaluate, and monitor AI applications to improve quality, reliability, latency, and cost.
 + Work with AI engineers, software engineers, architects, and DevSecOps teams to deliver maintainable production applications.