AI Engineer / GenAI Backend Developer

Role & Responsibilities

• Develop and maintain Python-based backend applications using FastAPI or Flask.
• Build practical Generative AI applications using LLMs, prompt engineering, embeddings, and context management.
• Develop RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking, embeddings, retrieval, and response generation.
• Build basic AI Agent and Agentic AI workflows using LangChain or LangGraph.
• Implement tool calling, API integrations, and REST APIs for AI-powered applications.
• Work with vector databases such as FAISS, ChromaDB, Pinecone, or similar technologies.
• Work with PostgreSQL/MySQL and basic NoSQL databases.
• Develop clean, reusable, and maintainable Python code following OOP and coding best practices.
• Use Docker, Git/GitHub, unit testing, logging, and debugging practices.
• Deploy and support applications on AWS or other cloud platforms.
• Test, debug, and troubleshoot backend and GenAI applications.
• Use AI coding assistants such as Claude, Cursor, GitHub Copilot, or similar tools effectively.
• Collaborate with the development team to design, develop, test, and deliver AI-powered solutions.
• Take ownership of assigned tasks and continuously learn emerging GenAI and Agentic AI technologies.

Preferred Candidate Profile

• 1 2 years of hands-on experience in Python/backend development or AI/GenAI application development.
• Strong knowledge of Python, OOP, data structures, and backend development.
• Hands-on experience with FastAPI or Flask and REST API development.
• Practical understanding of LLMs, Generative AI, prompt engineering, and embeddings.
• Experience building RAG applications and working with vector databases.
• Familiarity with LangChain or LangGraph, AI agents, workflows, and tool calling.
• Knowledge of PostgreSQL/MySQL and basic NoSQL concepts.
• Basic experience with AWS or other cloud platforms.
• Familiarity with Docker and Git/GitHub.
• Strong problem-solving, debugging, communication, and analytical skills.
• Ability to understand existing code, troubleshoot issues, and build features independently.
• Experience using AI-assisted coding tools such as Cursor, Claude, or GitHub Copilot is preferred.
• Candidates should have practical project experience, not just theoretical knowledge of GenAI concepts.
• Strong willingness to learn, adapt, and work across Python backend and GenAI technologies.