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# AI Engineer

- **Company:** Robert Half
- **Location:** Memphis, TN, United States
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** LangGraph Framework, Microsoft Windows, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Business Software, Cloud Computing, Databases, Continuous Integration, Monitoring of Systems, Python (Programming Language), Systems Development Life Cycle, OpenAI, Salesforce.Com, Software Deployment, Software Engineering, SQL Databases, Systems Integration, AI Infrastructure, Google Cloud, Enterprise Software Applications, Cloud Platform System, LangChain, Retrieval-Augmented Generation, Open-source Models, Large Language Models, Software Security, Model Validation, Software Application Programming, Llamaindex, Generative AI, Agentic-AI, Git, Containerization, Information Technology, Claude, Api Design, Restful APIs, Intelligent Document Processing, Servicenow, Microservices
- **Published:** November 5, 2026
- **Apply:** https://www.memphisjobforce.com/job.asp?id=3496680987&tx=JR9387FFQ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field, or equivalent practical experience.
 * 3+ years of software engineering experience, including hands-on AI/ML or generative AI development.
 * Strong proficiency in Python and API development.
 * Experience building applications using LLMs and generative AI technologies.
 * Hands-on experience with AI frameworks such as LangChain, LangGraph, LlamaIndex, or similar tools.
 * Experience developing AI agents, RAG applications, or workflow automation solutions.
 * Familiarity with REST APIs, SQL databases, and cloud environments.
 * Experience deploying applications in AWS, Azure, or Google Cloud.
 * Strong understanding of software development lifecycle (SDLC), Git, and CI/CD practices.

## Description

AI Development & Engineering

 * Design, develop, and deploy AI-powered applications and intelligent automation solutions.
 * Build customized AI agents capable of executing multi-step workflows, retrieving information, and interacting with enterprise systems.
 * Integrate large language models (LLMs), including OpenAI, Anthropic Claude, and open-source models, into business applications.
 * Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise knowledge repositories.
 * Implement AI solutions using Python, APIs, and modern AI frameworks such as LangChain, LangGraph, and LlamaIndex.
 * Evaluate and optimize AI model performance, accuracy, reliability, and cost.

Automation & Systems Integration

 * Identify opportunities to automate repetitive IT and business processes.
 * Build AI agents that integrate with enterprise applications, databases, cloud platforms, and internal APIs.
 * Develop automation workflows for IT support, infrastructure monitoring, reporting, and operational processes.
 * Integrate AI solutions with Microsoft 365, ServiceNow, Salesforce, ERP systems, and other enterprise platforms.
 * Partner with infrastructure and cybersecurity teams to ensure secure deployment and operation of AI solutions.

AI Infrastructure & Deployment

 * Deploy AI applications within AWS, Microsoft Azure, or Google Cloud environments.
 * Develop scalable APIs and microservices supporting AI applications.
 * Implement CI/CD pipelines, containerization, and monitoring for AI workloads.
 * Establish safeguards for data privacy, access control, model reliability, and responsible AI usage.
 * Monitor AI application performance and troubleshoot production issues.

Business Collaboration

 * Work directly with business leaders to identify AI use cases with measurable ROI.
 * Translate business requirements into practical technical solutions.
 * Develop proof-of-concept applications and transition successful solutions into production.
 * Document AI architecture, workflows, integrations, and operational procedures.
 * Provide technical guidance on AI adoption and emerging technologies.