AI and Knowledge Graph Engineer 

We are looking for an AI and Knowledge Graph Engineer to join our client’s team and contribute to building an AI-driven recruitment SaaS platform from scratch.

About the project

Our client is building an AI-driven, end-to-end recruitment SaaS platform from scratch, designed to transform and automate the entire recruitment lifecycle. The platform covers everything from capturing client requirements, validating and publishing job openings to evaluating candidates, managing recruiter-candidate communication, supporting interviews, negotiations, onboarding, and payroll processes.

At the core of the solution are advanced AI technologies, knowledge graphs, and ontologies that enable intelligent candidate matching based on skills, experience, and semantic relationships rather than traditional keyword-based searches.

The project also incorporates AI-powered communication, interview analysis, and sentiment analysis to streamline recruitment workflows and support more informed hiring decisions.

Your impact

You’ll join the engineering team to design, develop, and maintain production AI services and RDF-based Knowledge Graphs. You’ll work on information extraction, entity resolution, semantic search, and graph-based data processing, helping transform complex data into meaningful, structured knowledge.

You’ll contribute to technical decisions, improve data processing and retrieval capabilities, and ensure that AI-powered solutions are reliable, testable, and ready for real-world production environments.

What makes you a great fit

• 3+ years of experience in AI/ML engineering, Knowledge Graph Engineering, or Semantic Data Engineering
• Hands-on experience developing and deploying production AI services and RDF-based Knowledge Graphs
• Strong Python development skills, including FastAPI, Pydantic, and SQL
• Practical experience with RDF, SPARQL, ontology design, and semantic data modeling
• Experience with SHACL validation, taxonomies, and knowledge graph standards
• Hands-on experience with RDF graph databases such as GraphDB, Stardog, Apache Jena, Amazon Neptune, or similar
• Experience with NLP, information extraction, and entity resolution techniques
• Understanding of embeddings, semantic search, hybrid retrieval, and reranking approaches
• Experience building reliable data ingestion and transformation pipelines
• Strong understanding of automated testing, data validation, and production debugging
• Experience with asynchronous processing, timeouts, retries, and error handling
• Familiarity with model evaluation techniques, including precision, recall, and labeled datasets
• Upper-Intermediate English level or higher
• Strong analytical, problem-solving, and communication skills

Nice to have

• Experience with Microsoft Foundry, Azure ML, or MLflow
• Familiarity with Azure AI Search or OpenSearch
• Experience with PyTorch, sentence-transformers, spaCy, or scikit-learn
• Knowledge of SKOS, PROV-O, JSON-LD, and OWL reasoning
• Experience with Parquet, Delta Lake, or learning-to-rank approaches
• Understanding of event-driven architectures and distributed systems

What you will do

• Design, develop, and maintain production-ready Python services for information extraction, entity resolution, and semantic search
• Build and evolve RDF-based Knowledge Graphs, including ontology design, semantic relationships, provenance, and temporal context
• Develop and optimize SPARQL queries, graph ingestion pipelines, and RDF data validation processes
• Implement reliable data mapping and ingestion workflows, supporting incremental updates, idempotent processing, and full rebuilds
• Develop NLP pipelines to extract structured information from documents, forms, messages, and transcripts
• Implement entity matching, normalization, similarity scoring, and resolution mechanisms
• Build and improve lexical, vector, and hybrid search capabilities using embeddings, reranking, and graph queries
• Evaluate AI models and extraction pipelines using labeled datasets, precision/recall metrics, and reproducible testing
• Ensure reliability, performance, and maintainability through automated testing, monitoring, and fault diagnosis
• Apply responsible AI practices, including data minimization, prompt-injection testing, human review, and fallback mechanisms
• Collaborate with the engineering team on architecture, technical decisions, integrations, and continuous improvements

Perks & Benefits

• Vacation (up to 20 working days)
• Sick leave (10 working days)
• Up to 11 national holidays as a time off
• Medical reimbursement or insurance after 3 months
• Online English courses
• Flexible working schedule and remote work
• Direct cooperation with the customer
• Online and offline teambuildings
• Support for and development of our volunteering culture

Ready to take the next step? Apply now and let’s talk about how you can make an impact on the project.