Sr Python Full Stack Architect

Role Overview:

We are seeking a highly skilled Python Architect with deep expertise in backend development, GenAI/LLM integration, and cloud-native architecture. The ideal candidate will have hands-on experience with FastAPI, modern Python practices, and building scalable AI-driven applications using GenAI and RAG pipelines.

Key Responsibilities:

1. Core Python Development:

• Write clean, efficient, and scalable Python code.
• Deep understanding of:
  • Syntax, control flow, data types, I/O operations
  • args / **kwargs, scopes, packaging
  • Classes, inheritance, decorators, generators, descriptors
  • Async programming: event loop, tasks, aiohttp
  • Multithreading, multiprocessing, GIL, queues
  • Type hints, mypy, linting, testing (pytest, mocking, fixtures)
  • Performance profiling (cProfile), memory optimization
  • Serialization/deserialization

2. FastAPI & Web Frameworks:

• Expertise in FastAPI (preferred), Django or Flask
• Experience with:
  • Routers, Dependency Injection (DI), Pydantic models
  • Data validation, background tasks, events, middleware
  • ORM, signals, serializers, viewsets, permissions
  • HTTP verbs, status codes, schema design
  • API protocols: Protobuf, WSDL, XSD, GraphQL federation
  • Authentication: OAuth2/OIDC, JWT, scopes
  • Caching: cookies, dccstore, memcache

3. GenAI / LLM / RAG Integration:

• Experience with:
  • LLMs, tokenization, context windows
  • Prompting strategies: zero/one/few-shot
  • RAG pipeline: Load Chunk Embed Retrieve Generate
  • Vector search (HNSW/IVF), metadata filters
  • Handling toxicity, factuality, jailbreaks
  • Data cleaning, deduplication, PII handling
  • LangChain and other orchestration frameworks

4. Architecture & Design:

• Ability to design and explain:
  • Simple RAG architecture
  • Graph-based RAG architecture
  • End-to-end LLM solution diagrams
• Clearly articulate use cases and technical decisions

Preferred Qualifications:

• Experience with AWS (Bedrock, SageMaker, Lambda)
• Familiarity with Docker, ECS/EKS, CloudWatch
• Workflow automation tools: n8n, crewai, autogen
• Model tracking tools: wandb
• Strong communication and documentation skills