Senior Data Scientist (AI Platform)

TL;DR

Senior Data Scientist (AI Platform/MLOps): Designing and operating the machine learning lifecycle to turn models into reliable, production-grade capabilities with an accent on inference services, deployment pipelines, and monitoring frameworks. Focus on productionizing LLM systems, establishing MLOps standards, and ensuring the scalability and reliability of AI features.

Location: Hybrid (Budapest, Hungary)

Company

hirify.global is an edtech company dedicated to creating intuitive products that simplify learning and personal development.

What you will do

• Architect and operate scalable inference services, APIs, and backend components for LLM-powered product features.
• Productionize AI/ML workflows using MLOps practices: model versioning, testing, deployment pipelines, and monitoring.
• Define and implement evaluation frameworks for model quality, system reliability, latency, and cost.
• Build reusable platform patterns and service templates for adoption across multiple product teams.
• Establish and uphold engineering standards for code quality, observability, and incident readiness.
• Collaborate with infrastructure and research partners to move AI capabilities into production.

Requirements

• 6+ years of experience in software engineering, machine learning, or applied AI engineering.
• Demonstrated experience taking ML and LLM systems from prototype to production and operating them in live environments.
• Strong proficiency in building APIs and services (Python preferred) and working with containers.
• Expertise in MLOps: deployment and orchestration pipelines, artifact versioning, and rollback mechanisms.
• Working knowledge of modern AI patterns, including embeddings, retrieval, semantic search, and RAG.
• Must be based in Budapest, Hungary for a hybrid work arrangement.

Nice to have

• Experience with vector databases, retrieval infrastructure, or semantic indexing pipelines.
• Experience with graph databases or graph-based reasoning systems.
• Knowledge of observability and evaluation for LLM systems, including drift and failure analysis.
• Track record of creating engineering standards or templates adopted by multiple teams.
• Background in edtech, learning systems, or knowledge and skills modeling.

Culture & Benefits

• Competitive compensation and participation in an employee ownership program.
• Flexible work culture supporting remote, hybrid, and in-office collaboration.
• Generous time off, including the annual "Dim the Lights" recharge period in late December.
• Comprehensive wellness programs and mental health support.
• Professional development resources and tuition reimbursement.
• Inclusive culture featuring mentorship, hack weeks, and internal conferences.