1 month ago
Budapest, HungarySenior
Responsibilities
- Own the architecture and delivery of production-grade LLM systems and classical ML solutions.
- Design, evaluate, and optimize RAG pipelines, including retrieval strategy, chunking, indexing, and monitoring.
- Build scalable LLM services, agentic workflows, and traditional ML systems where appropriate.
- Define trade-offs among LLMs and traditional ML, fine-tuning and RAG, and hosted and self-managed models.
- Optimize distributed GenAI and ML workloads on Databricks, including Spark performance optimization.
- Implement evaluation frameworks for quality, hallucination, and performance measurement.
- Productionize systems with monitoring, rollback, versioning, and CI/CD practices.
- Design end-to-end AI solutions tailored to client problems and translate business needs into scalable architectures.
- Lead technical decisions in client engagements and contribute to pre-sales architecture discussions.
- Mentor team members and define GenAI and ML best practices.
Requirements
- At least 5 years of experience in Data Science or a related field.
- Proven experience delivering production LLM-based systems, beyond proof-of-concept work, plus classical ML project experience.
- Strong hands-on experience with RAG, agents, and open-source LLMs.
- Deep understanding of latency, cost, scaling, and reliability trade-offs, with experience optimizing production systems.
- Strong Python and SQL skills.
- Deep hands-on experience with Databricks and distributed computing with Spark, including performance optimization.
- Experience deploying scalable ML and LLM systems on AWS, Azure, or GCP.
- Ability to independently design end-to-end AI solutions for client problems.
- Client-facing experience and willingness to participate in pre-sales processes.
- Clear and confident English communication in technical discussions.
Benefits
- Mentoring and continuous professional development from the first day.
- Learning and development opportunities.
- International projects with clients and partners across global locations.
- Supportive corporate culture centered on collaboration, respect, and mutual support.
- Healthy work/life balance with reduced unnecessary meetings and administration.
Tech Stack
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