Artificial Intelligence Developer

Allegis Group; Hyderabad, Pune District, Bengaluru; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Allegis Group is seeking a Python developer to design and build GenAI-driven applications, including end-to-end RAG pipelines and AI interfaces. You will integrate LLMs, create REST APIs with FastAPI/Flask, and collaborate with product UX teams to deliver robust AI-powered solutions.

The role involves building with React/Streamlit/Gradio/Vue, optimizing LLM orchestration, and ensuring clean, testable Python code while adopting Docker, Git, and CI/CD practices.

Qualifications

• 3+ years of professional Python development experience with hands-on app development.
• Practical experience building LLM/GenAI applications, not just POC.
• Strong experience with RAG, embeddings, vector search, and LLM integration.
• Experience with LangChain, LangGraph, LlamaIndex or similar tools.
• Hands-on with UI tech such as React, Streamlit, Gradio, Vue or similar.
• Strong backend/API experience with FastAPI or Flask.
• Experience with vector DBs such as FAISS, Pinecone, Qdrant, Chroma or Weaviate.
• Good understanding of prompt engineering and LLM response optimization.
• Exposure to Docker, Git, CI/CD, unit testing, clean/modular architecture.
• Strong debugging, problem solving, communication and collaboration.
• End-to-end GenAI apps combining Python + LLM/RAG + API + UI is a plus.

Responsibilities

• Develop and maintain Python-based applications integrated with LLMs such as OpenAI, HuggingFace, Anthropic, and Ollama.
• Build end-to-end RAG pipelines, including document ingestion, chunking, embeddings, retrieval, and vector search.
• Develop GenAI application interfaces using React, Streamlit, Gradio, Vue, or similar UI frameworks.
• Build and integrate REST APIs using FastAPI or Flask to connect AI backends with front-end applications.
• Implement prompt engineering, LLM orchestration, and techniques to improve response quality and retrieval accuracy.
• Work with vector databases such as FAISS, Pinecone, Qdrant, Chroma, or Weaviate.
• Integrate AI capabilities into intuitive and responsive user experiences such as chatbots, AI assistants, dashboards, and document-based applications.
• Implement authentication, session management, and application state management for interactive AI solutions.
• Write clean, modular, testable, and well-documented Python code using engineering best practices.
• Use Git, Docker, and CI/CD practices for application development and deployment.
• Debug and optimize both LLM/backend performance and UI responsiveness.
• Collaborate with product and UX teams to convert designs and requirements into functional AI-powered applications.

Skills

Python development

LLM/GenAI apps

RAG pipelines

embeddings

vector search

UI frameworks

FastAPI/Flask

Docker

Git

CI/CD

unit testing

modular architecture

LLM integration

Tools

LangChain

LangGraph

LlamaIndex

FAISS

Pinecone

Qdrant

Chroma

Weaviate

Job description

Role & Responsibilities

• Develop and maintain Python-based applications integrated with LLMs such as OpenAI, HuggingFace, Anthropic, and Ollama.


• Build end-to-end RAG pipelines, including document ingestion, chunking, embeddings, retrieval, and vector search.


• Develop GenAI application interfaces using React, Streamlit, Gradio, Vue, or similar UI frameworks.


• Build and integrate REST APIs using FastAPI or Flask to connect AI backends with front-end applications.


• Implement prompt engineering, LLM orchestration, and techniques to improve response quality and retrieval accuracy.


• Work with vector databases such as FAISS, Pinecone, Qdrant, Chroma, or Weaviate.


• Integrate AI capabilities into intuitive and responsive user experiences such as chatbots, AI assistants, dashboards, and document-based applications.


• Implement authentication, session management, and application state management for interactive AI solutions.


• Write clean, modular, testable, and well-documented Python code using engineering best practices.


• Use Git, Docker, and CI/CD practices for application development and deployment.


• Debug and optimize both LLM/backend performance and UI responsiveness.


• Collaborate with product and UX teams to convert designs and requirements into functional AI-powered applications.

Preferred candidate profile

• 3+ years of professional Python development experience with strong hands-on application development.


• Practical experience building LLM/GenAI applications, not just theoretical or POC-level exposure.


• Strong hands-on experience with RAG, embeddings, vector search, and LLM integration.


• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, or similar.


• Hands-on experience with at least one UI technology such as React, Streamlit, Gradio, Vue, or similar.


• Strong backend/API experience with FastAPI or Flask and REST API development.


• Experience with vector databases such as FAISS, Pinecone, Qdrant, Chroma, or Weaviate.


• Good understanding of prompt engineering and LLM response evaluation/optimization.


• Exposure to Docker, Git, CI/CD, unit testing, and clean/modular architecture.


• Strong debugging, problem-solving, communication, and collaboration skills.


• Preferred candidates will have experience building end-to-end GenAI applications combining Python + LLM/RAG + API + UI