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:

- Solid 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).

📌 Senior AI Full Stack Engineer (Mumbai)
🏢 enreap
📍 Mumbai