Lead AI Engineer
$150,000–$250,000 year
On-sitePér, Győr-Moson-Sopron, Hungary
Job Summary
Design and lead implementation of AI-powered solutions using large language models, agentic AI, and workflow automation. Develop reference architectures, reusable frameworks, and engineering standards for AI initiatives while evaluating emerging tools for enterprise adoption. Drive adoption of AI-enabled engineering practices across development and QA teams, promoting effective use of modern AI development tools for code generation, testing, and deployment. Establish guardrails and best practices for responsible AI adoption, then measure productivity improvements through AI-driven workflows. Provide technical leadership to mentor teams, define AI roadmaps, and create implementation guidance and technical playbooks. Support successful implementation of AI enablement programs to embed AI into day-to-day workflows and accelerate innovation across business units.
Required Qualifications
- AI Engineering and Enablement Lead
- Drive AI adoption across software development, quality engineering, and related business functions
- Help teams design, implement, and scale practical AI solutions
- Play a key role in advancing AI-enabled software delivery
- Establish engineering best practices
- Enable business units to realize measurable value from AI technologies
- Combine hands-on AI engineering leadership, solution design, AI-SDLC enablement, and capability building
- Work closely with development, QA, and business stakeholders
- Help embed AI into day-to-day workflows
- Improve engineering productivity
- Accelerate innovation
- Participate in design and lead implementation of AI-powered solutions
- Use modern AI platforms, large language models, agentic AI, workflow automation, and related technologies
- Develop reference architectures, reusable frameworks, and engineering standards for AI initiatives
- Evaluate emerging AI tools, platforms, and technologies for practical enterprise adoption
- Guide teams in building scalable, secure, and maintainable AI solutions
- Drive adoption of AI-enabled engineering practices across development and QA teams throughout the development lifecycle
- Promote effective use of modern AI development tools such as Cursor, Claude Code, GitHub Copilot, and related technologies
- Enable AI-driven approaches for code generation, testing, debugging, documentation, analysis, refactoring, and deployment activities
- Establish engineering guardrails, standards, and best practices for responsible AI adoption
- Help teams measure and improve productivity through AI-enabled workflows
- Identify opportunities where AI can improve delivery efficiency, quality, and business outcomes
- Act as an enabler for R&D teams to adopt AI solutions in a practical, governed, and outcome-focused manner
- Design and support successful implementation of AI enablement programs
- Support AI-focused training and workshops to build practical adoption capabilities across teams
- Share best practices, lessons learned, and successful adoption patterns across teams
- Create implementation guidance, technical playbooks, and knowledge assets for AI engineering
- Promote a culture of experimentation, learning, and continuous improvement
- Provide technical leadership and mentorship to development and QA teams adopting AI technologies
- Collaborate with engineering leaders to define AI roadmaps and priorities
- Support solution planning and technical decision-making for strategic AI initiatives
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