Associate AI Full Stack Engineer

• Experience Level: 6 months\-1 year
• Function: Engineering — AI / Full Stack Development
• No. of vacancies: 4

• *About the Role**

We’re building GenAI\-powered applications that combine large language models, retrieval systems, and

cloud\-native infrastructure. We’re looking for early\-career engineers who have a strong grasp of AI/NLP

fundamentals and full\-stack development basics, and are eager to grow into building production\-grade AI

applications. You’ll work closely with senior engineers, ship real features, and ramp up fast.

• *What You’ll Do**

• Assist in building and integrating RAG pipelines and LLM\-powered features
• Develop front\-end interfaces using React under guidance from senior engineers
• Build and maintain backend APIs using Node.js/Python
• Work with vector databases and LangChain for retrieval\-based features
• Support deployment and testing of AI workloads on AWS
• Learn and follow team practices for code quality, version control, and testing
• Collaborate with product, design, and QA, and communicate progress/blockers clearly

• *Must\-Have Skills**
• *Programming \& AI/NLP Fundamentals**

• Proficiency in Python
• Understanding of NLP basics: tokenization, text preprocessing, POS tagging, NER
• Text vectorization concepts: BoW, TF\-IDF, Word2Vec/embeddings — should be able to explain and implement
• Conceptual understanding of transformer architecture (self\-attention, positional encoding)
• High\-level understanding of how LLMs (GPT/LLaMA\-class models) work
• Exposure to RAG concepts and vector databases (FAISS or ChromaDB) — academic or personal projects acceptable
• Basic hands\-on experience with LangChain (document loaders, text splitters, simple chains)

Full Stack Development

• React fundamentals — components, props, state, hooks, JavaScript/TypeScript, HTML, CSS
• Node.js basics — building simple REST APIs
• Git — clone, commit, branch, pull requests
• Basic SQL/NoSQL — CRUD operations Cloud (AWS) — Conceptual
• Conceptual awareness of Lambda, DynamoDB, and IAM — what they are and when they’re used

• *Soft Skills**

• Strong communication — written and verbal
• Willingness to learn independently and ask the right questions
• Problem\-solving attitude — takes initiative rather than waiting for exact instructions

• *Nice\-to\-Have**

• A personal or academic RAG/chatbot project (end\-to\-end, however small)
• Exposure to MCP, LangGraph, or agentic frameworks
• Basic fine\-tuning exposure (LoRA/QLoRA) via coursework or tutorials
• Self\-learned, free\-tier hands\-on experience with AWS Bedrock or SageMaker
• Any exposure to Atlassian Forge, Bitbucket, or Jest/unit testing

• *Department:** Delivery
• *Designation:** Engineer