Semantic Engineer

Boehringer Ingelheim GmbH; Buenos Aires; On-site; ARS 181,906,000 - 272,860,000; Full time

Job summary

Boehringer Ingelheim GmbH is seeking an experienced AI Semantic Architect to design and operate the semantic foundation powering enterprise-scale AI, intelligent agents, and knowledge-driven applications.

You will build the Enterprise Semantic Layer, unify enterprise knowledge across domains, and engineer reusable components like semantic services, MCP servers, and agent tooling for scalable AI systems. Collaboration with data engineers and business stakeholders is essential.

Qualifications

• 5+ years of software engineering, backend engineering, data platforms, or AI platform development.
• Strong proficiency in Python and modern API development frameworks.
• Hands-on experience with RDF, OWL, SPARQL, and SHACL.
• Experience with knowledge graphs or semantic layer technologies.
• Familiarity with Git workflows, CI/CD, testing, and cloud-native architectures.

Responsibilities

• Design, build, and operate the Enterprise Semantic Layer for AI and knowledge-driven apps.
• Develop knowledge graph and semantic layer architectures using RDF/OWL/SHACL.
• Create retrieval solutions using graphs, embeddings, metadata, and semantic search.
• Integrate enterprise knowledge graphs with Snowflake, Databricks, Neo4j, and vector DBs.
• Define testing, monitoring, and governance for semantic AI systems.
• Develop MCP servers, semantic tools, and agent-facing services.

Skills

Python

API development

Knowledge graphs

Semantic web

MCP servers

Git workflows

CI/CD

Cloud-native

Stakeholder communication

Regulated environments

Tools

Stardog

Metaphactory

Snowflake

Databricks

Neo4j

Job description

We are seeking an experienced AI Semantic Architect to design, build, and operate the semantic foundation that powers enterprise-scale AI, intelligent agents, and knowledge-driven applications.

This role is focused on the development of the Enterprise Semantic Layer, providing a unified and governed representation of enterprise knowledge across data domains, systems, and applications. Leveraging knowledge graphs, ontologies, semantic models, metadata systems, and retrieval technologies, you will build the technical capabilities that enable AI systems to discover, retrieve, reason over, and act upon enterprise knowledge.

Unlike ontology governance or data modeling roles, this position is focused on engineering reusable platform components such as semantic services, GraphRAG architectures, MCP servers, retrieval pipelines, and agent tooling that accelerate delivery of Enterprise Brain capabilities.

Tasks and responsibilities

Semantic Layer Engineering

• Design, implement, and evolve the Enterprise Semantic Layer that abstracts enterprise knowledge and makes it consumable by AI agents, applications, and analytical platforms.
• Translate heterogeneous enterprise data sources into a unified semantic representation using knowledge graphs, ontologies, semantic models, metadata, and business context.
• Design scalable architectures enabling interoperability across data domains through shared semantics and common business vocabularies.
• Ensure enterprise-grade security, governance, scalability, observability, and maintainability of semantic platform components.

Semantic Layer & KG Development

• Design and implement knowledge graph and semantic layer architectures using RDF, OWL, SHACL, SKOS, semantic models, and graph technologies.
• Develop retrieval solutions leveraging knowledge graphs, ontologies, embeddings, metadata, and semantic search capabilities.
• Support integration of enterprise knowledge graphs with platforms such as Snowflake, Databricks, Neo4j, vector databases, search engines, and AI platforms.
• Design and implement MCP servers, semantic tools, and reusable agent-facing services.
• Develop typed tool definitions and YAML/JSON-based tool specifications for enterprise agents.
• Build reusable frameworks and accelerators for Agentic AI solutions.
• Define testing, evaluation, monitoring, and governance approaches for semantic AI and agentic systems.

Required

• 5+ years of experience in software engineering, backend engineering, data platforms, or AI platform development.
• Strong proficiency in Python and modern API development frameworks.
• Hands‑on experience with semantic web technologies including RDF, OWL, SPARQL, and SHACL.
• Experience implementing and operating knowledge graph or semantic layer technologies.
• Strong understanding of software engineering best practices, Git workflows, CI/CD, testing, and cloud-native architectures.
• Ability to communicate effectively with data engineers, architects, AI engineers, and business stakeholders
• Experience with metaphactory, Stardog.
• Experience with Snowflake Semantic Models and Open Semantic Interoperability (OSI) compliant YAML specifications.
• Experience implementing MCP servers and agent platforms.
• Experience working in regulated environments such as pharmaceutical, healthcare, or life sciences industries.