UderTalk

Backend / AI Developer (Python, RAG, LangChain) at UnderTalk

We are looking for a Backend Developer to work on the RAG (Retrieval-Augmented Generation) system (intelligent agents based on LLM). The project is transitioning from an external team to internal development, so the main focus is on building quality architecture and scaling AI solutions.

Key tasks:

• Development and optimization of RAG agents using LangChain.
• Implementation of complex interaction logic with OpenAI API (GPT-4, Embeddings).
• Conducting a technical audit of existing code, fixing bugs, and refactoring.
• Ensuring data storage and analytics in PostgreSQL.

Technology stack and tools:

We are looking for a developer who not only knows Python but can build systemic solutions using modern AI-native approaches.

1. Core Backend & Architecture:

• Framework: FastAPI (asyncio, asynchronous development).
• Database & ORM: PostgreSQL + SQLAlchemy (model design, working with migrations).
• Architecture: Mandatory use of three-tier architecture (Routes-Services-Repositories). Clear separation of business logic and data access layer.
• Validation: Pydantic v2 for validation and data schemas.

2. AI & RAG Specialized:

• Framework: LangChain (our main tool for building agent logic).
• LLM: Deep integration with OpenAI API (GPT-4, Embeddings).
• RAG: Understanding of Retrieval-Augmented Generation mechanisms (working with context, search, reranking).

3. AI-Driven Development (Productivity Stack):

• IDE: Experience with Cursor (using AI features to speed up coding and refactoring).
• Cloud: Experience with CloudCode (integration with cloud infrastructure).
• Efficiency: Ability to use AI tools for automating test writing and documentation.

4. Infrastructure:

• Containerization: Docker and Docker-compose (preparing development and deployment environments).
• CI/CD: Working with Git (GitHub/GitLab), understanding code delivery processes.

Conditions:

• Remote work with a flexible schedule.
• No unnecessary bureaucracy and the ability to influence the tech stack.
• No micromanagement.

To apply:

• Link to GitHub (preferably with examples of code on FastAPI or LangChain).

• Current resume.

• Brief description of your experience with RAG: what tools you used, which databases you worked with, and how you addressed response quality issues (e.g., working with context or prompts).

• Salary expectations (USD/hour).

The selection process includes a short technical task (up to 2 hours) to check coding culture.