Full Stack Python Developer for AI & Crypto Market Analysis

ABOUT THE ROLE We are looking for a versatile Full Stack Python Developer to take ownership of a production-grade crypto market-structure analysis platform. The system combines real-time OHLCV data ingestion, algorithmic swing/trend detection (BoS, CHoCH, Wyckoff), AI-generated market summaries, and an interactive Streamlit front end — all backed by a Django REST API and a multi-agent LLM pipeline. You will be the primary engineer: maintaining and extending existing detection algorithms, improving the AI summarization pipeline, hardening authentication and integrations, and shipping new visualization features. This is a full-ownership role — you will work across backend, frontend, and AI layers. WHAT YOU WILL WORK ON - Maintain and improve the Django 5 async REST API (django-ninja), domain-separated apps (AI, chat, stock, auth, chart, users) and Pydantic schema layer. - Extend swing-point and trend detection algorithms (BoS/CHoCH, Wyckoff zones) built on pandas DataFrames — improving accuracy, coverage, and performance. - Develop and tune the LLM/AI pipeline: LangChain agents, LangGraph orchestration, OpenAI integration, and Langfuse prompt tracing/observability. - Build and refine the Streamlit interactive UI: Plotly candlestick charts, overlays, session state management, and browser-side token persistence. - Own the Celery + Redis background task layer, async job scheduling, and health-check infrastructure. - Manage exchange API integrations (Bybit public API patterns), token-refresh auth flows, and REST ingestion helpers. - Maintain Docker-based dev and production infrastructure (multi-stage builds, compose stacks, MinIO/S3, uWSGI). - Write and expand the pytest test suite; enforce code quality with ruff, pre-commit hooks, and type annotations. REQUIRED SKILLS & EXPERIENCE - Python 3.11+ — strong command of modern Python: type hints, dataclasses, enums, packaging (pyproject.toml / Poetry / UV). - Django (v5) — async patterns, modular app design, django-ninja or DRF, ORM with custom querysets/managers. - Pandas & NumPy — time-series manipulation, rolling/EWM operations, index-based masking on OHLCV data. - Streamlit — production deployments, session state, component lifecycle, local storage integration. - LLM / AI engineering — LangChain, agent/tool design, structured outputs, prompt engineering; OpenAI API experience required. - Plotly — candlestick charts, graph_objects overlays, interactive financial visualizations. - REST API design — token-based auth, OAuth/refresh patterns, Pydantic request/response schemas. - Celery + Redis — background task design, scheduling (celery-beat), broker/result-backend configuration. - Docker — multi-stage Dockerfiles, compose stacks, service orchestration. - Testing & quality — pytest, factory_boy, ruff linting, pre-commit, CI pipelines. - Trading/market-structure basics — knowledge of or willingness to learn swing highs/lows, Break of Structure (BoS), Change of Character (CHoCH), and Wyckoff methodology. NICE TO HAVE - LangGraph and LangFuse (or equivalent LLM observability stack) hands-on experience. - Bybit or other crypto exchange API integration experience. - Pydantic v2, OpenTelemetry, and Sentry instrumentation in a production setting. - MinIO / S3-compatible object storage configuration. - PostgreSQL with psycopg3, async queries, and geospatial extensions (GDAL/GEOS). - RS256 JWT key management and cryptography-library-level security work. - Experience serializing dataframes as LLM context (CSV-based prompt construction). TECH SNAPSHOT - Backend: Python 3.13, Django 5, django-ninja, Celery, Redis, PostgreSQL, uWSGI - Frontend: Streamlit, Plotly (graph_objects), streamlit-local-storage - AI / LLM: LangChain, LangGraph, OpenAI, Langfuse, prompt engineering - Data: Pandas, NumPy, ta (technical analysis), Pydantic - Infra: Docker, MinIO/S3, OpenTelemetry, Sentry, JWT (RS256) - Quality: pytest, factory_boy, ruff, pre-commit, Poetry / UV