AI Agent Developer Job Lincolnshire area,Illinois USA,IT/Tech

This role focuses on the design, development, and deployment of AI agents that support business operations, automation, and customer-facing use cases. The ideal candidate brings hands-on experience building and deploying AI-driven solutions in real-world environments, with the ability to translate business needs into scalable, production-ready agent architectures.

Key Responsibilities

Design, develop, and deploy AI agents using modern LLM platforms (e.g., OpenAI, Claude, Gemini, Azure AI)
Implement agent architectures including tool use, multi-step workflows, and retrieval-augmented generation (RAG) patterns
Integrate AI agents with enterprise systems, APIs, and data sources
Develop and maintain reusable components such as prompt libraries, evaluation frameworks, and agent workflows
Support deployment and lifecycle management, including monitoring, versioning, and performance tuning
Implement guardrails, validation, and security practices to ensure responsible AI usage
Collaborate with engineering, data, and business teams to refine requirements and deliver solutions
Stay current with emerging tools, frameworks, and best practices in AI agent development

Required Skills & Experience

5+ years of software development experience, including hands-on work with AI/LLM-based solutions
Proficiency in Python and/or JavaScript/Type Script
Experience building and deploying AI agents or LLM-driven applications in production environments
Strong understanding of LLM concepts, prompt engineering, and agent design patterns
Experience integrating APIs, data sources, and backend services into AI workflows
Familiarity with cloud platforms (AWS, Azure, or GCP) and modern development practices
Strong communication skills and ability to work with both technical and non-technical stakeholders

Preferred Skills & Experience

Experience with agent frameworks (e.g., Lang Chain, Lang Graph, Semantic Kernel)
Familiarity with vector databases, embeddings, and RAG architectures
Experience with CI/CD pipelines, containerization, or cloud-native deployment models
Exposure to AI observability, evaluation, or testing frameworks
Experience implementing AI security practices (e.g., prompt injection mitigation, data protection)