AI Engineer

Metas Solutions•, GA

About The Position

Metas Solutions has an "immediate opening" for a talented AI Engineer to 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.

Requirements

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 Haves

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

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