AI Agent Developer

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 ResponsibilitiesDesign, 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) patternsIntegrate AI agents with enterprise systems, APIs, and data sourcesDevelop and maintain reusable components such as prompt libraries, evaluation frameworks, and agent workflowsSupport deployment and lifecycle management, including monitoring, versioning, and performance tuningImplement guardrails, validation, and security practices to ensure responsible AI usageCollaborate with engineering, data, and business teams to refine requirements and deliver solutionsStay current with emerging tools, frameworks, and best practices in AI agent development Required Skills & Experience5+ years of software development experience, including hands-on work with AI/LLM-based solutionsProficiency in Python and/or JavaScript/TypeScriptExperience building and deploying AI agents or LLM-driven applications in production environmentsStrong understanding of LLM concepts, prompt engineering, and agent design patternsExperience integrating APIs, data sources, and backend services into AI workflowsFamiliarity with cloud platforms (AWS, Azure, or GCP) and modern development practicesStrong communication skills and ability to work with both technical and non-technical stakeholders Preferred Skills & ExperienceExperience with agent frameworks (e.g., LangChain, LangGraph, Semantic Kernel)Familiarity with vector databases, embeddings, and RAG architecturesExperience with CI/CD pipelines, containerization, or cloud-native deployment modelsExposure to AI observability, evaluation, or testing frameworksExperience implementing AI security practices (e.g., prompt injection mitigation, data protection)