Senior AI Full Stack Engineer

Own the full lifecycle of GenAI-powered products — from model & RAG integration to production-grade full-stack delivery. Experience · 3–5 years Function: Engineering-AI+Full Stack We're building GenAI-powered applications that combine large language models, retrieval systems, and cloud-native infrastructure. We're looking for an engineer who can own the full lifecycle — from model and RAG integration through to production-grade full-stack development — and ship independently with minimal oversight. What You'll Do: — Design and build end-to-end architecture for AI-powered applications, from UI through backend to cloud infrastructure. — Develop RAG pipelines, integrate LLMs, and build MCP-based agentic workflows. — Build responsive, production-quality front-end interfaces using React. — Develop and maintain backend services and APIs using Node.js and Python. — Deploy, scale, and monitor AI workloads on AWS. — Evaluate and monitor LLM/RAG output quality in production. — Partner closely with product, design, and QA to translate requirements into shipped features. — Troubleshoot independently and propose solutions — not just surface problems. Must-Have Skills: • 3–5 years in software / full-stack development. • Proficiency in Python. Full Stack Development: • Proficiency in React, JavaScript/TypeScript, HTML, and CSS. • Backend development with Node.js and RESTful API design. • SQL/NoSQL databases, Git, and version control (GitHub or Bitbucket). AI & NLP: • Strong NLP foundation: tokenization, preprocessing, POS tagging, NER, vectorization (BoW, TF-IDF, Word2Vec/embeddings). • Solid grasp of transformer architecture (self-attention, multi-head attention, positional encoding) and how LLMs are trained. • Hands-on experience building RAG systems, including hybrid search. • Prompt engineering — designing, testing, and iterating on prompts for production. • Vector databases (FAISS, ChromaDB, or Pinecone). • Working knowledge of LangChain and MCP (Model Context Protocol). Cloud-AWS/Atlassian: • Practical experience with core AWS services: Lambda, Bedrock, DynamoDB, and IAM. • Hands-on experience with the Atlassian platform (Jira / Confluence / JSM). • Experience integrating with Atlassian REST APIs and app development (Forge or Connect). Soft Skills: • Excellent written and verbal communication skills. • Ability to work independently and drive problems to resolution. Good to Have — a strong candidate need not check every box. • LangGraph, CrewAI, AutoGen, or similar frameworks for stateful, multi-agent applications. • LLM/RAG evaluation and observability tooling (e.g., RAGAS, LangSmith). • Fine-tuning experience (LoRA/QLoRA, quantization) on open models such as Gemma. • Atlassian Forge platform (UI Kit / Custom UI, resolvers, manifest.yml, Forge Storage/SQL). • Jira / Confluence / JSM REST APIs and OAuth 2.0 app scopes. • SageMaker, EC2, Cognito, or S3. • Containerization and CI/CD (Docker, GitHub Actions, or equivalent). • API security — rate limiting, input validation, prompt-injection mitigation for LLM-facing endpoints. • Unit testing experience (Jest or equivalent).