GenAI Engineer – Cursor AI Developer JD

GenAI Engineer – Cursor AI Developer JD

Job Title: Senior Generative AI Engineer – Cursor / AI-Assisted Development
Experience: 6–10+ years overall, with 3+ years in GenAI/LLM engineering
Location: Atlanta GA or Richmond VA
Employment Type: Full-Time

Job Summary

We are looking for a Senior Generative AI Engineer with hands-on experience using Cursor AI to accelerate software development, build enterprise GenAI applications, and integrate LLM-powered capabilities into modern engineering workflows. The ideal candidate will have strong software engineering fundamentals along with experience in LLMs, RAG, Agentic AI, prompt engineering, AI coding assistants, and cloud-native application development.

Key Responsibilities

• Design, develop, and deploy Generative AI and LLM-powered applications for enterprise use cases.
• Use Cursor AI extensively for AI-assisted software development, code generation, refactoring, debugging, documentation, test generation, and codebase understanding.
• Establish effective Cursor workflows and development standards for enterprise engineering teams.
• Develop AI applications using OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or open-source LLMs.
• Build RAG solutions using vector databases, embeddings, semantic search, hybrid search, metadata filtering, and enterprise knowledge sources.
• Design and implement AI agents and agentic workflows using frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent technologies.
• Develop REST APIs and AI services using Python, FastAPI, Flask, or similar frameworks.
• Integrate LLM applications with enterprise APIs, databases, SaaS platforms, repositories, and internal systems.
• Implement prompt engineering, context engineering, structured outputs, function/tool calling, and model evaluation.
• Develop automated test cases and documentation using AI-assisted development practices.
• Use Cursor to analyze large codebases, identify dependencies, modernize legacy code, and accelerate application migration/refactoring.
• Implement AI guardrails, security controls, access management, PII protection, and responsible AI practices.
• Build evaluation frameworks to measure accuracy, relevance, hallucination, latency, cost, and response quality.
• Containerize and deploy GenAI applications using Docker and Kubernetes.
• Work with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Implement CI/CD pipelines and automated quality checks for AI-enabled applications.
• Collaborate with architects, developers, DevOps, data engineers, security teams, and business stakeholders.