Semantic AI Engineer

Reposted 4 Days Ago
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Hiring Remotely in Sofia, Sofia-grad, BGR
Remote
Mid level
Artificial Intelligence • Information Technology • Software
The Role
As a Semantic AI Engineer, you'll design data pipelines, enhance AI accuracy with graph-based patterns, and develop knowledge extraction workflows while collaborating with clients.
Summary Generated by Built In

At Graphwise, we help enterprises transform fragmented data into connected, intelligent systems using Knowledge Graphs, semantic technologies, and modern AI architectures.

About the role

We’re looking for a strong Software Engineer with experience in data engineering and an interest in AI systems, data modeling, and large-scale information architectures. You don’t need to be a semantic technologies expert already - what matters most is solid engineering thinking, curiosity, and the ability to work with complex data problems.

Main Responsibilities:

  • Design and build robust data pipelines for structured and unstructured data
  • Integrate and harmonize data from multiple enterprise systems
  • Work on AI-oriented retrieval and context architectures, including RAG and GraphRAG patterns
  • Build workflows for extracting structured information from documents and text
  • Contribute to scalable backend and data processing systems
  • Collaborate with technical and business stakeholders to solve complex information challenges
  • Explore and adopt modern AI, NLP, and data engineering technologies

Must-haves:

  • Strong software engineering fundamentals
  • Professional experience with Python, Java, or Scala
  • Experience building backend systems or data pipelines
  • Solid understanding of data modeling and ETL processes
  • Familiarity with modern AI concepts such as: LLMs, RAG, vector databases, embeddings, or NLP workflows
  • Experience with Git, CI/CD, and collaborative engineering practices
  • Strong analytical and problem-solving skills
  • Good communication skills in English
  • Curiosity and willingness to learn new domains and technologies

Nice-to-haves:

  • Knowledge Graphs or graph databases
  • Semantic technologies such as RDF, OWL, SHACL, or SPARQL
  • Ontology or taxonomy modeling
  • NLP Basics: Basic understanding of Knowledge Extraction, specifically identifying and linking entities within text.
  • NLP tooling such as SpaCy or similar libraries
  • GraphRAG implementations
  • Cloud platforms and distributed systems

Skills Required

  • Programming skills in Python, Java, or Scala
  • Strong foundation in data modeling, ETL processes, and exploratory data analysis
  • Solid understanding of LLM principles and architectures
  • Familiarity with Retrieval-Augmented Generation and Vector databases
  • Basic understanding of Knowledge Extraction and entity linking
  • Experience with Git version control and CI/CD
  • Excellent English communication skills
  • Passion for continuous learning
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The Company
HQ: New York, New York
157 Employees
Year Founded: 2024

What We Do

Graphwise enables organizations to unlock ROI for enterprise AI by delivering the most comprehensive and trusted industry solution in the field of knowledge graphs and semantic AI technologies. As enterprises pour millions into AI investment, Graphwise delivers the critical knowledge graph infrastructure to ensure enterprises are ready to realize the technology’s full potential, is trusted, and can be implemented at scale. Graphwise, which is the result of the merger between tech visionaries Ontotext and Semantic Web Company, has over 200 employees worldwide, with offices located across North America, Europe and APAC.

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