Key Responsibilities

• Build API endpoints — write DRF serializers and viewsets serving data to a React frontend, working with complex models (JSONFields, custom managers, mixin patterns).

• Extend AI agent capabilities — add new tools to agents; work on context engineering, and retrieval systems.

• Write async task workers — build and debug distributed RQ worker logic covering document parsing (PDF, Excel, PowerPoint) and LLM inference pipelines.

• Improve test coverage — write pytest tests with real database fixtures, mock external APIs, and catch N+1 query issues.

• Ship frontend features — build React/TypeScript components wired to TanStack Query, styled with SCSS Modules; work with rich text, interactive charts, and virtualized tables.

• Debug AI output — diagnose and fix hallucinations and retrieval failures in agent pipelines.

Technical Requirements

Required

• Solid Python fundamentals — classes, tracebacks, data structures, without over-relying on AI autocomplete.

• Familiarity with web APIs: HTTP methods, JSON serialization, request/response cycles.

• Comfort with Git: branching, rebasing, resolving merge conflicts.

• Experience with at least one relational database: SQL queries and basic schema design.

• Comfortable working with AI-based tools like Cursor, Claude Code, etc.

• Genuine curiosity about AI/ML — LLM API usage, a RAG pipeline, or serious experimentation beyond chatting with ChatGPT.

• Ability to debug AI-generated code — shipping broken AI output is worse than writing it yourself.

Nice to Have

• Django or Flask experience.

• React/TypeScript exposure, even from a personal project.

• Familiarity with Docker and containerized development.

• Experience with vector databases, embeddings, or LLM orchestration (LangChain, LangGraph, etc.).

• A deployed project you can demo — valued more than GPA.

Tech Stack

• Backend: Python 3.13, Django 5.2, Django REST Framework, PostgreSQL, Redis.

• AI/ML: OpenAI, Anthropic & Google LLM APIs; LangChain + LangGraph; sentence-transformers; RAGAS.

• Data: NumPy, Pandas, Polars, scikit-learn, XGBoost, PyMC, Prophet, Plotly.

• Frontend: React 19, TypeScript, Vite, Ant Design, TanStack Query, SCSS Modules.

• Infra: Docker, GitHub Actions CI/CD, AWS (S3, ECR), MinIO, Sentry, RQ.

What We Offer

• Mentorship and hands-on learning alongside senior engineers.

• Opportunity to ship production code from day one on a real AI platform.

• A culture that embraces AI tooling, rigorous testing, and continuous tech innovation.