AI Engineer
⚲ Gdańsk, Warszawa, Łódź
30 240 - 33 600 PLN (B2B)
Wymagania
- Python
- ML/AI
- Generative AI
- Cloud experience
Opis stanowiska
O projekcie:
The AI Engineer will design, build, and scale AI solutions, working closely with stakeholders to deliver use cases from concept to production.
Wymagania:
- Master Degree in Computer Science, Engineering, Data Science, or equivalent- 5+ years building and deploying Machine Learning/AI solutions in production- Strong Python skills and scalable system development experience- Hands-on with Agentic AI (e.g., LangGraph, CrewAI, AutoGen)- Experience with Generative AI (LLMs, RAG, MCP)- Strong AI/ML system design skills (model selection, deployment, monitoring)- Cloud experience (AWS, Azure, or GCP)- Proven track record from idea → PoC → scalable product- Strong communication with both technical and business stakeholders
Nice-to-have knowledge and experience:
- Experience with A2A protocols and multi-agent systems- Knowledge of agent testing, monitoring, and reliability- Understanding of AI safety, alignment, and ethics- Familiarity with EU AI regulations- Experience in regulated industries (e.g., banking, finance)
Codzienne zadania:
- Develop and scale AI applications end-to-end
- Prototype and validate AI use cases; move from PoC to production
- Build Agentic and Generative AI solutions (LLMs, RAG, MCP, A2A)Integrate AI with vector databases, APIs, and enterprise systems
- Explore new AI tools and create PoCs
- Deploy and monitor solutions on cloud platforms (AWS, Azure, GCP)
- Ensure proper documentation, compliance, and risk management
- Promote ethical and responsible AI practices
- Mentor team members and share best practices around Artificial Intelligence
The AI Engineer will design, build, and scale AI solutions, working closely with stakeholders to deliver use cases from concept to production.
Wymagania:
- Master Degree in Computer Science, Engineering, Data Science, or equivalent- 5+ years building and deploying Machine Learning/AI solutions in production- Strong Python skills and scalable system development experience- Hands-on with Agentic AI (e.g., LangGraph, CrewAI, AutoGen)- Experience with Generative AI (LLMs, RAG, MCP)- Strong AI/ML system design skills (model selection, deployment, monitoring)- Cloud experience (AWS, Azure, or GCP)- Proven track record from idea → PoC → scalable product- Strong communication with both technical and business stakeholders
Nice-to-have knowledge and experience:
- Experience with A2A protocols and multi-agent systems- Knowledge of agent testing, monitoring, and reliability- Understanding of AI safety, alignment, and ethics- Familiarity with EU AI regulations- Experience in regulated industries (e.g., banking, finance)
Codzienne zadania:
- Develop and scale AI applications end-to-end
- Prototype and validate AI use cases; move from PoC to production
- Build Agentic and Generative AI solutions (LLMs, RAG, MCP, A2A)Integrate AI with vector databases, APIs, and enterprise systems
- Explore new AI tools and create PoCs
- Deploy and monitor solutions on cloud platforms (AWS, Azure, GCP)
- Ensure proper documentation, compliance, and risk management
- Promote ethical and responsible AI practices
- Mentor team members and share best practices around Artificial Intelligence
🔍 Dekoder Ogłoszenia
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deliver use cases from concept to production
Oczekuje się, że kandydat samodzielnie przeprowadzi cały cykl życia projektu AI, od początkowego pomysłu aż po wdrożenie produkcyjne.
🔴
5+ years building and deploying Machine Learning/AI solutions in production
Wymagane jest znaczące, praktyczne doświadczenie w wdrażaniu systemów AI na produkcji, a nie tylko teoretyczna wiedza.
🔴
Hands-on with Agentic AI (e.g., LangGraph, CrewAI, AutoGen)
Oczekiwane jest praktyczne, codzienne używanie konkretnych narzędzi do budowy systemów agentowych, a nie tylko ogólne zrozumienie koncepcji.
🔴
Proven track record from idea → PoC → scalable product
Kandydat musi udowodnić, że potrafi nie tylko tworzyć prototypy, ale także przekształcać je w skalowalne, gotowe do użycia produkty.
🟡
Strong communication with both technical and business stakeholders
Oprócz umiejętności technicznych, kluczowa jest zdolność do efektywnego komunikowania się z osobami nietechnicznymi i rozumienia ich potrzeb.