AI Engineer

DCG is a space where business needs and people's ambitions meet. They help candidates find an environment to develop and help companies build effective teams by closely listening and responding to what matters to them, creating lasting and valuable relationships.

• Minimum 5 years of software engineering experience including production AI systems or advanced data-based solutions.
• Strong knowledge of LLM, RAG, AI agents, tool integrations, and practical aspects of deployment and maintenance.
• Experience designing and developing complex AI systems including APIs, application services, data pipelines, information retrieval, evaluation processes, and cloud infrastructure.
• Ability to define and monitor measurable AI solution quality metrics considering business value, reliability, performance, and costs.
• Practical experience in AI-assisted engineering, Spec-Driven Development, and modern software development tools.
• Strong system architecture knowledge and design of scalable, reusable components.
• Experience in cross-functional collaboration from concept and prototyping to production deployment.
• Ability to support other engineers' development, raise technical standards, and work in a dynamic environment.
• Excellent communication and collaboration skills.
• Higher technical education or equivalent professional experience.

Nice to have:

• Knowledge of Python, AWS, Azure, LLM, GenAI, RAG.
• Proficiency in Polish and English languages.

• Create and continuously develop shared AI competencies, reusable services, and engineering accelerators for multiple software products in the construction industry.
• Design and implement production AI systems including LLM-based solutions, retrieval pipelines, tool integrations, orchestration layers, and AI agent workflows.
• Develop technical artifacts executable by humans and machines such as specifications, evaluation methods, interface definitions, and workflow contracts to improve solution transparency, speed, and quality.
• Define and apply evaluation-driven development cycles for AI systems covering quality, reliability, latency, costs, grounding quality, and other key performance indicators.
• Collaborate with product teams to identify valuable cross-product opportunities and support integration of shared functionalities.