AI Full stack Developer
Summary
Build and maintain AI-powered full-stack apps using LLMs, RAG, and vector databases, with React frontends, Python/Node.js backends, and cloud-native DevOps.
AI Full Stack Engineer
Krakow, Poland (Hybrid 2-3 days in a week)
6 Months contract with possible extension (Sub-con or B2B contract) Experience Strong software engineering experience with Full Stack and AI ML development.
Role Description
Experience Strong software engineering experience with Full Stack and AI ML development.
Responsibilities
- Design develop and maintain endtoend AIpowered applications across frontend backend and AIML layers
- Build and operate LLMenabled solutions using prompting RAG toolfunction calling evaluation frameworks and guardrails
- Develop APIs and microservices for integrating AI models with enterprise platforms and data sources
- Create and manage data pipelines for data ingestion transformation indexing and retrieval
- Build responsive user interfaces for AIpowered assistants chat applications dashboards and workflow solutions
- Implement MLOps and LLMOps practices including CICD model lifecycle management monitoring and quality validation
- Develop testing strategies covering unit testing integration testing performance testing and AI evaluation
- Optimize AI applications for performance scalability cost efficiency reliability and observability
- Ensure security privacy compliance and governance requirements are integrated into solution design
- Collaborate with product UX data engineering and risk teams to deliver business outcomes
- Develop reusable AI components best practices and platform accelerators
To Be Successful In This Role You Should Have
- Strong Full Stack software engineering experience
- Expertise in Python Java or Nodejs development
- Experience building RESTful APIs GraphQL services and Microservices architectures
- Strong frontend development skills using React and TypeScript
- Handson experience with Large Language Models LLMs Embeddings and RetrievalAugmented Generation RAG
- Experience with vector databases search platforms document stores or graph databases
- Strong SQL and data engineering skills
- Experience with Docker Kubernetes and cloud platforms such as AWS Azure or GCP
- Knowledge of CICD Infrastructure as Code and DevOps practicesExperience with monitoring logging and observability solutions
- Understanding of secure coding practices OWASP principles authentication and authorization
- Strong problemsolving collaboration and stakeholder management skills
- Experience with Agentic AI orchestration frameworks and tool integrations is desirable
- Knowledge of Responsible AI model governance and explainability concepts is a plus
- Experience working in regulated enterprise environments is preferred