THE ROLE
Our Senior Applied AI Engineer builds and operate production-grade AI systems that extract meaning from large-scale unstructured document collections, enabling enterprise data discovery classification, and governance.
This role owns the full lifecycle of graph intelligence solutions — from problem definition and data modelling, to building and enriching knowledge graphs, and deploying ML- and LLM-assisted analytics in production. The focus is on semantic and contextual analysis of unstructured data to uncover relationships, patterns, and insights that support AI safety, security, and compliance requirements.
WHAT YOU'LL DO
- Design, build, and deploy graph-based AI solutions, combining knowledge graphs, LLMs, and ML models applied to large-scale unstructured data
- Define and own data pipelines that extract, transform, and enrich entity relationships into production-grade knowledge graphs
- Integrate LLMs and ML models into text processing pipelines for classification, embedding generation, document similarity, and semantic analysis
- Design, deploy, and operate graph and vector databases to support retrieval, reasoning, and analytics
- Optimize models and inference pipelines for production constraints including latency, throughput, cost, and infrastructure
- Deploy, monitor, and iterate on ML systems in production environments ensuring reliability and continuous integration
- Drive architectural decisions and technical direction for applied AI and graph intelligence solutions
- We are primarily an in-office environment and therefore, you will be expected to work from the Prague office in compliance with Everpure's policies, unless you are on PTO, or work travel, or other approved leave.
WHAT YOU BRING
- Proven experience in developing and deploying to production knowledge graphs in a real business scenario
- Strong hands-on background in developing text-based ML and LLM systems, including prompt context engineering, optimized for production environments
- Strong proficiency in Python, ML / KG frameworks and tools (e.g. Neo4j, NetworkX, Node2Vec, SentenceTransformers) with the ability to select the right tool for the problem at hand
- Ability to apply the appropriate graph algorithms and design choices for problem solving, including relationship and ontology modelling, feature definition, supervised vs unsupervised graph analytics
- Ability to design clean, modular, and testable ML code in a collaborative environment
- Experience with MLOps practices, building production-grade Docker images, and development of AI solutions designed for integration into production systems
- Experience collaborating with software engineers, product managers, and business stakeholders
- Strong communication skills and ability to explain ML concepts and system behavior clearly