4 hours 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 architecture trade-offs involving LLMs versus traditional ML, fine-tuning versus RAG, and hosted versus self-managed models.
- Optimize distributed GenAI and ML workloads on Databricks, including Spark performance optimization.
- Implement evaluation frameworks for quality, hallucination, and performance.
- Productionize systems with CI/CD, monitoring, rollback, and versioning.
- 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 establish GenAI and ML best practices.
Requirements
- At least 5 years of experience in Data Science or a related field.
- Proven experience delivering LLM-based systems to production, beyond proof-of-concept work.
- Experience with classical ML projects and strong hands-on experience with RAG, agents, and open-source LLMs.
- Deep understanding of system-level trade-offs involving latency, cost, scaling, and reliability, 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 in response to client problems.
- Client-facing experience and willingness to participate in pre-sales processes.
- Clear and confident English communication in technical discussions.
Benefits
- Mentoring from the first day and continuous career support through a dedicated mentoring system.
- Learning and development opportunities for ongoing professional growth.
- International projects with clients and partners worldwide, including potential work involving New York, the Netherlands, Sweden, and Scotland.
- Supportive, collaborative corporate culture emphasizing comradery, mutual support, and respect.
- Healthy work/life balance and reduced unnecessary meetings and administration.
- Exposure to advanced technologies and diverse technical challenges.
Tech Stack
Categories
About Hiflylabs
Hiflylabs provides data engineering, analytics, AI/ML, and mobile application development for enterprises, delivering data warehouses, BI platforms, and cloud solutions. The privately held firm, founded in 2015 and headquartered in Budapest, operates as a consulting and implementation partner across AWS, Azure, Snowflake, and Databricks, where it holds partner status and MVP credentials. It serves customers in finance, telecom, energy, and other sectors in Europe and North America.