Python AI Engineer / GenAI Backend Developer (1 – 2 Years)

Bitcot Inc.; San Diego (CA); On-site; USD 100,000 - 150,000; Full time

Job summary

Bitcot Inc. is seeking a Python AI Engineer / GenAI Backend Developer to join our team in San Diego. You will build practical AI applications by combining Python backend engineering, Generative AI, RAG, and agentic AI.

The ideal candidate should have hands-on experience with Python, FastAPI/Flask, LLMs, RAG, LangChain or LangGraph, vector databases, REST APIs, and cloud platforms. You will contribute to AI-powered applications, API integrations, and reliable backend services.

Qualifications

• Hands-on experience with Python backend engineering and AI integration.
• Familiarity with LLMs, RAG, embeddings, and AI agents.
• Experience building AI-powered backend services and APIs.

Responsibilities

• Build and debug practical GenAI applications using Python backend.
• Develop and integrate AI agents, RAG workflows, and embeddings.
• Collaborate across backend and GenAI components, ensuring robust APIs.

Skills

Python

FastAPI/Flask

LLMs

RAG

LangChain

LangGraph

Vector databases

REST APIs

Cloud platforms

Docker

Git/GitHub

Unit testing

Tools

AWS

PostgreSQL/MySQL

Job description

We are looking for some fresh talents

We are looking for a Python AI Engineer / GenAI Backend Developer to join our team.

We are looking for an AI Engineer / GenAI Backend Developer with 1 to 1.5 years of experience who can build practical AI applications by combining Python backend engineering, Generative AI, RAG, and Agentic AI.

The ideal candidate should have hands‑on experience with Python, FastAPI/Flask, LLMs, RAG, LangChain or LangGraph, vector databases, REST APIs, and cloud platforms. They should be comfortable building AI-powered applications, integrating APIs and tools, and developing reliable backend services.

Key Areas:

• Agentic AI: AI agents, basic LangGraph workflows, tool calling, and agent workflows


• Generative AI: LLMs, prompt engineering, embeddings, context management, and basic AI evaluation


• RAG: Document ingestion, chunking, embeddings, retrieval, and vector databases


• Backend: Python, FastAPI/Flask, REST APIs, OOP, and clean coding practices


• Cloud: Basic experience with AWS or other cloud platforms


• Databases: PostgreSQL/MySQL, NoSQL basics, and familiarity with vector databases


• Engineering: Docker, Git/GitHub, unit testing, debugging, logging, and API development


• AI-Assisted Development: Claude, Cursor, GitHub Copilot, or similar AI coding tools

What We’re Looking For:

We’re looking for engineers who can build and debug practical GenAI applications, not just understand AI terminology.
You should have a strong foundation in Python and backend development, along with practical knowledge of LLMs, RAG, embeddings, and AI agents. You should be willing to learn new AI technologies and work across backend and GenAI components.
Strong problem-solving, debugging, communication, learning ability, and ownership are essential. We also value developers who use AI coding assistants to improve productivity while taking full ownership of the code, testing, security, and technical decisions.

Let us empower your business, improve your efficiency, and give you the competitive advantage!