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Generalist mid-level Fullstack role with broad AI, frontend and backend requirements increases competition.
General fullstack skills are transferable, but specialized GenAI/RAG expertise reduces cross-industry fit.
Explicit 3–5 years plus mandatory full-stack and GenAI tech requirements enforce strict candidate filters.
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Own end-to-end development and deployment of GenAI-powered applications including UI, backend, and cloud infrastructure.
Design and build Retrieval-Augmented Generation (RAG) pipelines, integrate large language models (LLMs), and develop agentic workflows based on Model Context Protocol (MCP).
Deploy, scale, monitor AI workloads on AWS and evaluate LLM/RAG output quality in production.
3-5 years of professional experience in software or full-stack development.
Proficiency in Python, React, JavaScript/TypeScript, Node.js, and RESTful API design.
Strong foundational NLP knowledge including tokenization, POS tagging, NER, vectorization techniques and hands-on experience with transformer architecture and RAG systems.
Practical experience with AWS core services (Lambda, Bedrock, DynamoDB, IAM) and Atlassian platform (Jira, Confluence, JSM).
Experienced mid-level engineer capable of independent end-to-end ownership of full stack GenAI products.
Strong interdisciplinary skills bridging AI/NLP models, front-end React development, backend Node.js/Python, and cloud operations on AWS.
Familiar with advanced AI frameworks (LangChain, MCP), vector databases, prompt engineering, and hands-on integration with Atlassian REST APIs for scalable production environments.