- 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