Sr Software Engineer - Knowledge Representation (Ontology and ML Engineer)

Essential Duties:

• Collaborate with internal and external stakeholders including users, developers, product managers, leadership, etc., to understand needs and challenges and translate these into solutions.
• Lead, design & develop scalable structures and databases to capture, store and query structured, semi-structured and unstructured data.
• Lead, design and maintain ontologies and data architectures to represent complex relationships and entities.
• Mentor junior engineers.
• Integrate graph solutions with other data stores and technologies.
• Leverage LLMs/GenAI and rules engines for data I/O and data curation.
• Multimodal data handling: incorporate text, images, videos, tables, graphs, rules, etc., into the knowledge representation system.
• Proactively explore new techniques and emerging trends to drive adoption of new solutions.
• Develop and maintain software tools to support knowledge representation needs.
• Implement tools for evaluating and monitoring solution performance.
• Invent and deploy novel solutions with maintainable APIs, MCP servers, to suit stakeholder needs.
• Adhere to sound software engineering practices.

Competencies:

• Solid foundational understanding and application of Computer Science principles.
• Proficiency in knowledge graph technologies (e.g. RDF, LPG, SPARQL, Cypher)
• Understanding of data modeling, ontology design, data architecture
• Familiar with LLMs and other machine learning and agentic solutions.
• Familiar with all steps of SDLC and software development best practices.
• Excellent software engineering skills including unit testing, modular problem decomposition, multiple solution integration, etc.
• Fluent with scripting and query languages.
• Requires minimal to no supervision.

Required Qualifications and Skills:

• Minimum 8 years working as a software engineer or data scientist, 6 years with a Master’s degree.
• Expertise with one or more Graph query languages (SPARQL, Cypher, Gremlin) and the associated Graph Databases.
• Experience with Tabular (SQL), Document (NoSQL), and Vector/Semantic databases and their interactions with Graph DBs.
• Expertise with Python & SQL.
• Experience with AWS cloud resources (S3, EC2, ECS, Lambda).
• Experience with Docker or other container services.
• Specification and creation of APIs and microservices.