Senior Machine Learning Engineer (Hungary)

RESPONSIBILITIES

• Develop and implement production-ready AI agents that support core healthcare workflows, such as booking appointments, managing follow-up care, and assisting with patient account access.
• Create structured conversation flows using real patient data to support meaningful, context-aware interactions.
• Collaborate with colleagues from Engineering Foundations, Product, Design, and other Engineering teams to identify high-impact use cases and shape the development of AI agents.
• Contribute to the full development lifecycle: from data collection and testing ideas to building prototypes, sharing demos, and releasing features into production.
• Apply NLP and LLM-based techniques to build components such as sentiment analysis tools, real-time conversation insights, and behavior tuning systems.
• Design quality assurance tools to help monitor and improve the consistency and performance of AI-powered conversations.
• Work within a modern machine learning operations (MLOps) environment that supports scalability and reliability.
• Support predictive analytics features, such as patient no-show prediction and sentiment dashboards, to drive better patient experiences.
• Clearly communicate machine learning concepts and project updates to both technical and non-technical team members.
• Take ownership of tasks, contribute to team progress, and deliver high-quality work in a collaborative and fast-paced setting.

REQUIREMENTS

• Bachelor’s degree in a STEM discipline or equivalent practical experience. A Master’s or PhD may be considered in place of some industry experience.
• Minimum of 5 years of professional experience in applied machine learning or AI engineering. Candidates with advanced degrees may qualify with fewer years of experience.
• Demonstrated success deploying machine learning models into production environments—not limited to research or proof-of-concept projects.
• Proficiency in Python or TypeScript, with strong working knowledge of SQL for managing and analyzing large datasets.
• Hands-on experience with large language models (LLMs) and natural language processing (NLP) tools or libraries (e.g., TensorFlow, Hugging Face, LangChain).
• Experience working with cloud-based infrastructure, ideally Amazon Web Services (AWS).
• Familiarity with MLOps practices and tools, such as workflow orchestration using Airflow, Dagster, or similar platforms.
• Excellent collaboration and communication skills; comfortable working closely with cross-functional teams including product managers, designers, and engineers.

BONUS

• Experience developing consumer-facing AI agents, particularly within regulated industries such as healthcare or finance.
• Background in real-time analytics or large-scale data processing.
• Familiarity with Snowflake or other modern data warehouse platforms.