Artificial Intelligence Engineer

Onsite IT Advisors; Atlanta (GA); On-site; USD 150,000 - 210,000; Full time

Job summary

Onsite IT Advisors seeks a senior AI-enabled web application engineer to design and deliver mission-critical solutions. You will build retrieval-based knowledge assistants, chatbots, and agentic workflows using Azure AI Foundry, Azure OpenAI, and related tools.

You will collaborate with enterprise architects, product owners, AI engineers, data engineers, and DevSecOps teams to ensure secure, scalable, and maintainable production applications for complex client challenges.

Qualifications

• 5+ years in software engineering or related technical discipline.
• 1+ years building generative AI or agentic AI solutions, including LLM APIs and RAG.
• Experience with Python, APIs, data pipelines, and web frameworks.
• Experience with REST APIs, JSON interfaces, authentication, and external integrations.
• Experience with at least one major cloud platform (Azure or AWS).
• Familiar with Git, GitHub, Jira, PRs, code reviews, and Agile/Scrum.
• Knowledge of LLM fundamentals: tokens, context windows, prompts, outputs.
• U.S. Citizenship or Lawful Permanent Residency required.

Responsibilities

• Define and deliver mission-critical AI-enabled web applications.
• Build AI apps using Azure AI Foundry, Azure OpenAI, and Foundry Agent Service.
• Develop RAG systems, knowledge assistants, chatbots, and agentic workflows.
• Implement agent and LLM workflows with tools, guardrails, and human review.
• Integrate AI apps with APIs, data sources, databases, and cloud services.
• Develop secure, cloud-native services and UIs using Python and containers.
• Test, evaluate, and monitor AI apps to improve quality, latency, and cost.

Skills

Generative AI

LLM APIs

Prompt engineering

Tool/function calling

RAG systems

APIs & data pipelines

Cloud platforms (Azure/AWS)

Agile/Scrum

Stakeholder communication

Public Trust suitability

Education

Bachelor's degree in CS/Engineering

Tools

Python

Git

GitHub

Jira

Azure

Job description

In this role, you will define and deliver mission-critical AI-enabled web applications that address complex, high-impact client challenges. You will apply modern generative AI techniques to design and build retrieval-based knowledge assistants, chatbot experiences, and agentic workflows that integrate data processing, orchestration, and multi-agent components behind intuitive user interfaces. You will collaborate with enterprise and solutions architects, product owners, AI engineers, data engineers, and DevSecOps teams to deliver secure, scalable, and maintainable production solutions. Your technical expertise will help clients translate emerging AI capabilities into practical applications that improve how people access information, make decisions, and carry out their work. A strong preference will be given to candidates located in Atlanta, GA with an openness for candidates located in Washington, DC.

Key Responsibilities:

• 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.

Must Have:

• 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
• Experience 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)

Nice to Have:

• 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
• Experience 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
• Ability to clearly communicate technical problems, implementation decisions, and trade-offs to both technical and non-technical stakeholders