AI Engineer (Databricks)

Location: Budapest, Bartók Béla út, Hungary

Company

hirify.global is a Budapest-based data and technology consultancy delivering data engineering, data science, artificial intelligence, application development, and analytics projects for clients worldwide.

What you will do

• Own the architecture and delivery of production-grade LLM systems and classical ML solutions.
• Design, evaluate, and optimize RAG pipelines, including retrieval, chunking, indexing, and monitoring.
• Build scalable LLM services, agentic workflows, and distributed GenAI and ML workloads on Databricks using Spark and MLflow.
• Define trade-offs between models, fine-tuning, RAG, hosted services, and self-managed solutions while optimizing latency, cost, scalability, and reliability.
• Productionize systems with CI/CD, monitoring, rollback, versioning, and evaluation frameworks for quality and hallucination.
• Design AI solutions for client problems, lead technical decisions and pre-sales architecture discussions, and mentor team members.

Requirements

• 5+ years of experience in data science or a related field.
• Production experience delivering LLM-based systems and classical ML projects.
• Strong hands-on experience with RAG, agents, and open-source LLMs.
• Strong Python and SQL skills, plus deep experience with Databricks, Spark, and distributed computing performance optimization.
• Experience deploying scalable ML and LLM systems on AWS, Azure, or GCP.
• Clear, confident English communication in technical discussions and experience working directly with clients.

Nice to have

• Experience with multiple AI and ML architecture patterns and technology ecosystems.
• Specialization in a specific industry or domain.

Culture & Benefits

• International projects with clients and partners across different countries.
• Mentoring from the first day and ongoing professional development support.
• Access to advanced technologies and challenging data and AI projects.
• Autonomous, trust-based work environment with emphasis on work-life balance.
• Supportive culture based on collaboration, respect, and mutual support.