- 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 robust 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 rapid.

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

📌 Associate AI Full Stack Engineer (India)
🏢 enreap
📍 India