Senior Python Engineer

Senior Python Engineer — financial backend development and integrations for UK clients. Team-based work, daily deployments, work conducted in English.

Responsibilities

• Design, development, and maintenance of scalable backends in Python (APIs, data pipelines, async workers)
• Implementation and integration of external systems and APIs
• Writing high-quality, well-tested code (unit, integration, functional)
• Maintaining CI/CD and daily production deployments
• Code reviews, upholding best practices and code quality
• Mentoring juniors and mid-level developers, pair programming, knowledge sharing
• Identifying risks, bottlenecks, and security issues; implementing solutions
• Collaborating with Product Managers and Tech Leads; estimating tasks
• Using AI tools in the development cycle: code generation, refactoring, automated reviews, test creation, debugging

Tech Stack

• Language: Python 3.9+; JS/TypeScript, React (mile widziane)
• Web/API: FastAPI, Django REST Framework, Flask
• Databases: PostgreSQL, Redis, MongoDB, MySQL
• Task queues: Celery, RabbitMQ, Kafka
• AI/LLM: Anthropic Claude, OpenAI, LangChain, LlamaIndex, BMAD, SpecKit, OpenSpec
• AI tooling: Claude Code, Cursor, GitHub Copilot
• Testing: pytest
• Infrastructure: Docker, GitLab CI/CD, AWS, GCP
• Version control: Git (GitLab)

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Requirements

Must have:

• 6+ years professional Python (post-graduation)
• ≥0.5 years commercial AI tools on projects: agents, MCP integrations, skill crafting, Spec-driven integration, SDLC with agents, or RAG chatbots
• Strong Python 3.9+ engineering: async/await, dataclasses, Pydantic, NumPy, Pandas
• API/integration: REST, GraphQL, third-party systems
• Automated tests: unit, integration, E2E
• Ability to start solo on new project; drive client communication; grow team if needed
• Experience with FastAPI, Django REST Framework or Flask
• Relational DBs (PostgreSQL) and ORMs (SQLAlchemy, Django ORM)
• Architecture: clean architecture, DDD, CQRS, event-driven systems
• CI/CD (GitLab CI, GitHub Actions); containerization (Docker, Docker Compose)
• Personalized AI workflow: custom skills, prompt templates, project-level instructions
• AI coding assistants: Claude Code, Cursor, GitHub Copilot or similar
• Prompt engineering: produce structured, reliable output; evaluate AI-generated code
• Critical evaluation of AI output
• Passion for clean code, peer reviews, high code quality
• Scaling: vertical scaling, single-tenant deployments
• Autonomy; comfortable in open team cultures
• AWS experience (RDS, S3, Kubernetes, SQS, RabbitMQ)
• Ready to work on Windows (WSL)