GenAI & Agentic AI Consultant (LLM Specialist) We are seeking a highly skilled GenAI & Agentic AI Consultant to contribute to the rapid development of our LLM-powered SaaS platform. This is a contract-based , fully remote opportunity focused on delivering real-world AI solutions with LLM, Agentic AI, LangGraph, and GenAI stacks . 🔧 Key Responsibilities Lead the development and delivery of MVP and critical AI modules (e.g., blog engine, AI dashboard, automation system) Architect and integrate reusable modules including RSS scrapers, rewrite engines, and token-optimized AI APIs Collaborate with cross-functional teams including Product, Design, DevOps, and QA Utilize LLMs, LangChain, LangGraph, and AI orchestration frameworks (Agentic AI, MCP) Maintain Agile workflows and sprint cycles using Jira, Trello or equivalent tools Design scalable microservices-based architecture using modern frameworks (e.g., Next.js, CopilotKit, serverless environments) Implement CI/CD pipelines, performance optimizations, caching strategies, and security measures Contribute to SEO-aware AI content workflows, scraping/crawling logic, and smart automation Ideal Qualifications Proficiency in LLM architecture , Agentic AI , and GenAI-driven SaaS solutions 5+ years of experience in software or AI product development, preferably in SaaS Strong hands-on expertise in machine learning, NLP, and prompt engineering Demonstrated experience in content platforms, AI pipelines, or automation engines Familiarity with Next.js , microservices , serverless architectures , and API-first development Skilled in SEO, web scraping, AI-powered crawling, and browser automation Experience with tools like n8n , MCP , LangGraph , and LLM evaluation Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field Strong communication and independent execution skills in a remote-first environment This role is an excellent opportunity to work on cutting-edge AI SaaS solutions and shape the next wave of intelligent web platforms. Show more Show less