Python Developer AI
⚲ Warszawa, Gdańsk, Wrocław, Kraków, Białystok, Poznań, Łódź, Lublin, Szczecin, Olsztyn
18 480 - 23 520 PLN netto (B2B)
Wymagania
- Python
- AI
- GitHub
- Claude
- LLM
- Git
- Agile
- Jira
Opis stanowiska
Python Developer with strong AI skills
You will be responsible for:
• Design and develop AI agents supporting data engineering workflows.
• Build agent capabilities directly within modern IDE environments.
• Automate repetitive data engineering tasks using agentic solutions.
• Integrate agents with data platforms, pipelines, and developer tools.
• Create reusable prompts, tools, and workflows for engineering teams.
• Collaborate with data engineers to identify automation opportunities.
• Optimize agent performance, reliability, and response quality.
• Test, validate, and continuously improve agent behavior.
• Monitor agent usage and implement feedback-driven enhancements.
• Document agent architectures, best practices, and implementation guidelines.
Nice to have:
• Knowledge of data processing tools and standards (dbt, ODCS, ODPS)
What you will need:
• Strong Python development skills with a focus on automation and developer tooling.
• Hands-on experience with AI coding assistants such as GitHub Copilot, Claude Code, Cursor, or similar tools.
• Practical knowledge of agentic AI concepts, including tools, skills, prompts, and multi-step workflows.
• Experience designing and implementing MCP (Model Context Protocol) integrations.
• Understanding of LLM capabilities, limitations, and prompt engineering best practices.
• Familiarity with integrating agents with APIs, databases, and enterprise systems.
• Proficiency with Git and modern software development workflows.
• Experience working with Agile teams and tools such as Jira.
• Strong problem-solving skills and a passion for improving developer productivity.
• Good communication skills and professional proficiency in English.
You will be responsible for:
• Design and develop AI agents supporting data engineering workflows.
• Build agent capabilities directly within modern IDE environments.
• Automate repetitive data engineering tasks using agentic solutions.
• Integrate agents with data platforms, pipelines, and developer tools.
• Create reusable prompts, tools, and workflows for engineering teams.
• Collaborate with data engineers to identify automation opportunities.
• Optimize agent performance, reliability, and response quality.
• Test, validate, and continuously improve agent behavior.
• Monitor agent usage and implement feedback-driven enhancements.
• Document agent architectures, best practices, and implementation guidelines.
Nice to have:
• Knowledge of data processing tools and standards (dbt, ODCS, ODPS)
What you will need:
• Strong Python development skills with a focus on automation and developer tooling.
• Hands-on experience with AI coding assistants such as GitHub Copilot, Claude Code, Cursor, or similar tools.
• Practical knowledge of agentic AI concepts, including tools, skills, prompts, and multi-step workflows.
• Experience designing and implementing MCP (Model Context Protocol) integrations.
• Understanding of LLM capabilities, limitations, and prompt engineering best practices.
• Familiarity with integrating agents with APIs, databases, and enterprise systems.
• Proficiency with Git and modern software development workflows.
• Experience working with Agile teams and tools such as Jira.
• Strong problem-solving skills and a passion for improving developer productivity.
• Good communication skills and professional proficiency in English.
🔍 Dekoder Ogłoszenia
🔴
Design and develop AI agents supporting data engineering workflows.
Może oznaczać tworzenie prostych skryptów automatyzujących zadania, a nie zaawansowanych systemów AI.
🔴
Build agent capabilities directly within modern IDE environments.
Może sugerować pracę z narzędziami typu Copilot, a nie tworzenie od podstaw skomplikowanych modeli AI.
🔴
Automate repetitive data engineering tasks using agentic solutions.
Główny nacisk może być położony na automatyzację, a nie na głębokie innowacje w dziedzinie AI.
🔴
Practical knowledge of agentic AI concepts, including tools, skills, prompts, and multi-step workflows.
Wymaga praktycznego zastosowania istniejących narzędzi AI, a niekoniecznie tworzenia nowych algorytmów.
🔴
Experience designing and implementing MCP (Model Context Protocol) integrations.
MCP nie jest powszechnie znanym standardem, co może oznaczać wewnętrzny protokół firmy lub niszową technologię.