We are seeking a highly skilled Computational Linguist with expertise in ontology development and knowledge graph implementation. This role will be pivotal in shaping our data infrastructure and ensuring the accurate representation and integration of complex domain knowledge into an agentic ecosystem. You will leverage industry best practices to design, develop, and maintain ontologies and knowledge graphs. We view these components as foundational to the modernization of our digital infrastructure and your value to our team will reflect that conviction.

Job Purpose: 

Your role is to help us transform a categorical taxonomy that maps customer intents to servicing flows into an ontological representation of the domain of knowledge required to accomplish the same task. You will also be required to provide insight on how this critical piece of infrastructure should be shaped in order to best fit within the current ML, annotation, and analytics pipelines being used within our product area.

Key Responsibilities: 

Design and apply ontology principles to improve semantic reasoning and data integration, ensuring alignment with business requirements and industry standards.  Design frameworks for incorporating knowledge graphs within a model architecture designed for classification and extraction. Collaborate with domain experts, product managers and customers to capture and formalize domain knowledge into ontological structures and vocabularies & improve data discoverability. Develop and maintain comprehensive ontologies to model various business entities, relationships, and processes. Create\Build Knowledge Graph based on the ontologies while ensuring data integrity and data consistency. Utilize knowledge graphs to enable advanced analytics, search, and recommendation systems. Ensure the quality, accuracy, and consistency of ontologies, and knowledge graphs. Define and implement data governance processes and standards for ontology development and maintenance. Collaborate with internal engineering teams to align data architecture with Gen AI capabilities Leverage on AI techniques by aligning knowledge models with RAG pipelines and agent orchestration Work closely with data scientists, software engineers, and business stakeholders to understand their data requirements and provide tailored solutions.

 

Required Qualifications:

Master’s degree (or equivalent experience) in Computational Linguistics, NLP, Linguistics, or a related field  2–3 years of hands-on experience building ontologies and knowledge systems. Proficiency with graph databases such as Neo4j, GraphDB [RDF based]. Understanding of semantic standards like OWL, RDF, W3C and property graph approaches. 2+ years of experience in NLP or AI projects (industry or research)  At least one year of experience with Gen AI  Familiarity with LLM behavior, prompt-based evaluation, and generative model outputs  Proficiency in Python and NLP/data science libraries: pandas, numpy, scikit-learn, NLTK  Comfortable with structured data formats (JSONL, CSV), Jupyter notebooks, and pandas-based analysis  Experience using Git for version control and collaborative development   Strong written communication skills for documenting experiments and results  Experience working in cross-functional or research-oriented teams

Preferred Qualifications

At least one year of experience with Gen AI and Agentic AI (e.g. AutoGen, LangGraph, CrewAI, Sierra) At least one year of experience with agentic AI frameworks and evaluation Experience using and fine-tuning transformer-based language models (e.g., BERT, GPT)  Familiarity with Gen AI concepts like retrieval-augmented generation and agent-based AI. Experience working with public datasets (e.g. Hugging Face, Kaggle)  Understanding of model evaluation methodologies, including human-AI comparison and red teaming  Experience working with financial institutions/financial products and services