Why This Role Stands Out
This hybrid role offers a significant opportunity to shape the future of AI by designing and deploying cutting-edge GenAI solutions, including autonomous agents and advanced RAG systems. You'll thrive here if you're an experienced AI Engineer passionate about pushing the boundaries of LLM applications and eager to mentor others while working with leading technologies in a collaborative environment.
Quick Overview
Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
3 weeks ago
DockerFastAPIAWSAzureGenerative AIGoogle CloudLLMPythonREST
Job Description
AI Engineer – GenAI / Agentic Systems
Duration: 6–18 Months
Job Summary
STAFFXPERT LLC is seeking an AI Engineer – GenAI / Agentic Systems on behalf of our client in Charlotte, NC or Dallas, TX. The ideal candidate will have hands-on experience designing, developing, and deploying production-grade Generative AI solutions, with strong expertise in agentic AI, GraphRAG, LLM applications, and modern AI engineering practices.
Key Responsibilities
Duration: 6–18 Months
Job Summary
STAFFXPERT LLC is seeking an AI Engineer – GenAI / Agentic Systems on behalf of our client in Charlotte, NC or Dallas, TX. The ideal candidate will have hands-on experience designing, developing, and deploying production-grade Generative AI solutions, with strong expertise in agentic AI, GraphRAG, LLM applications, and modern AI engineering practices.
Key Responsibilities
- Design, build, and deploy production-grade GenAI applications using foundation models and advanced architectures such as GraphRAG.
- Develop autonomous AI agents using frameworks such as Google ADK, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar technologies.
- Take GenAI solutions from proof of concept through production while ensuring scalability, reliability, observability, and maintainability.
- Design and implement RAG and GraphRAG pipelines using embeddings, vector databases, knowledge graphs, and enterprise data sources.
- Develop scalable REST APIs using Python and FastAPI.
- Containerize and deploy AI services using Docker and cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Implement LLM evaluation frameworks to measure response quality, groundedness, latency, and hallucination rates.
- Apply LLMOps practices including CI/CD, prompt and model version management, automated testing, monitoring, and observability.
- Collaborate with engineering teams to integrate AI capabilities into enterprise platforms.
- Mentor engineers and contribute to GenAI development standards and best practices.
- Evaluate emerging GenAI technologies, agent frameworks, and industry trends.
- Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience.
- 5+ years of software engineering experience, including recent experience developing GenAI or LLM-powered applications.
- Proven experience taking GenAI applications from proof of concept to production.
- Hands-on experience with modern agent development frameworks such as Google ADK, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar.
- Strong Python development skills, including FastAPI and REST API development.
- Experience implementing RAG or GraphRAG solutions.
- Experience with embeddings, vector databases, knowledge graphs, and enterprise data integration.
- Experience deploying AI workloads using Docker and AWS, Azure, or Google Cloud Platform.
- Familiarity with LLMOps practices, including CI/CD, prompt management, model governance, monitoring, and evaluation.
- Experience with LLM evaluation tools such as LangSmith, Ragas, DeepEval, or equivalent.
- Strong communication, collaboration, analytical, and problem-solving skills.
- Experience building enterprise-scale AI platforms and applications.
- Experience with production observability and AI application monitoring.
- Strong understanding of emerging agentic AI architectures and multi-agent systems.
- Demonstrated ability to evaluate and adopt emerging GenAI technologies.
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