Senior AI Full Stack Engineer

enreap; Pune District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

enreap is building GenAI-powered applications that integrate LLMs, RAG pipelines, and cloud-native infrastructure. You will own the full lifecycle from model integration to production-grade full-stack delivery.

We seek an engineer with 3–5 years of software experience, strong Python skills, and the ability to design end-to-end architectures. Expect collaboration across product, design, and QA and independent problem-solving.

Qualifications

• 3–5 years in software/full-stack development.
• Proficiency in Python.

Responsibilities

• Design end-to-end architecture for AI-powered applications from UI to cloud infrastructure.
• Develop RAG pipelines, integrate LLMs and MCP-based workflows.
• Build production-quality front-ends with React and backend APIs with Node.js/Python.
• Deploy, scale, and monitor AI workloads on AWS.
• Collaborate with product, design, and QA to ship features.

Skills

Experience 3–5 years

Python

NLP fundamentals

Transformer architectures

Prompt engineering

LangChain

MCP (Model Context Protocol)

Vector databases

LLMs in production

Independent problem-solving

Tools

Node.js

RESTful API design

SQL/NoSQL

Git

Job description

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:

• 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.
• 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).

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.
• Jira / Confluence / JSM REST APIs and OAuth 2.0 app scopes.
• 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).