Computational Linguist, Generative AI - Sr. Associate
On-site · Wilmington, Delaware, United States
Wilmington, Delaware, United StatesOn-siteFull TimeSenior LevelMasters DegreeFinancial ServicesEnterprise
Type
Full Time
Level
Senior Level
Education
Masters Degree
Company size
Enterprise
Industry
Financial Services
Job Summary
Senior Computational Linguist for Generative AI evaluation and integration of LLM-powered workflows. Role involves designing scalable evaluation processes, managing taxonomy for intents/entities, integrating LLMs with existing conversational AI architectures, developing metrics and guardrails across model correctness and customer experience, maintaining artifact suites for LLMs, ontology design for semantic reasoning, knowledge-graph integration, collaboration with data scientists, software engineers, and business stakeholders, and driving continuous improvement in model performance and data quality for customer-facing applications in financial services.
Required Qualifications
- Master's degree in Computational Linguistics, NLP, Linguistics, or related field
- 2+ experience in Computational Linguistics or NLP applied to chatbot or conversational AI development
- Hands-on experience with Generative AI and Agentic AI frameworks and evaluation (e.g., AutoGen, LangGraph, CrewAI, Sierra)
- Linguistic background in discourse & pragmatics
- Advanced knowledge of conversational AI product development lifecycle — training, design, conversation analysis
- Hands-on experience with LLM integration, prompt engineering, evaluation, and performance monitoring
- Proficient in Python, Git, Linux, and Bash scripting
- NLP/data science libraries: pandas, numpy, scikit-learn, NLTK
- Experience with transformer-based models (e.g., BERT, GPT) — fine-tuning and application
- Experience with generative AI SDKs and frameworks (e.g., OpenAI, Google, Anthropic, LangChain)
- Comfortable with JSONL, CSV, and Jupyter notebook workflows
- Experience with ontologies/taxonomies and knowledge graphs
- Solid understanding of evaluation methodologies including human-AI comparison and red teaming
- Direct experience with financial institutions, financial products, and customer-facing queries
- Chase customer service experience highly desirable
- Strong written communication for documenting experiments, results, and processes
- Experience with hybrid conversational architectures, generative AI, and LLM-driven flow design
- Familiarity with LLM safety, bias, and compliance
- Demonstrated success in a highly matrixed organization
- Awareness of current GenAI trends and evaluation challenges in subjective NLP tasks
- Solve the cold start problem via synthetic data generation for new intents, flows, and low-resource scenarios
Desired Qualifications
- Master's degree in Computational Linguistics, NLP, Linguistics, or related field
- 2+ years of experience in Computational Linguistics or NLP applied to chatbot or conversational AI development
- Hands-on experience with Generative AI and Agentic AI frameworks and evaluation (e.g., AutoGen, LangGraph, CrewAI, Sierra)
- Linguistic background in discourse & pragmatics
- Advanced knowledge of conversational AI product development lifecycle — training, design, conversation analysis
- Hands-on experience with LLM integration, prompt engineering, evaluation, and performance monitoring
- Proficient in Python, Git, Linux, and Bash scripting
- Experience with NLP/data science libraries: pandas, numpy, scikit-learn, NLTK
- Experience with transformer-based models (e.g., BERT, GPT) — fine-tuning and application
- Experience with generative AI SDKs and frameworks (e.g., OpenAI, Google, Anthropic, LangChain)
- Comfortable with JSONL, CSV, and Jupyter notebook workflows
- Experience with ontologies/taxonomies and knowledge graphs
- Solid understanding of evaluation methodologies including human-AI comparison and red teaming
- Direct experience with financial institutions, financial products, and customer-facing queries
- Chase customer service experience highly desirable
- Strong written communication for documenting experiments, results, and processes
- Experience with hybrid conversational architectures, generative AI, and LLM-driven flow design
- Familiarity with LLM safety, bias, and compliance
- Demonstrated success in a highly matrixed organization
- Awareness of current GenAI trends and evaluation challenges in subjective NLP tasks
- Solve the cold start problem via synthetic data generation for new intents, flows, and low-resource scenarios
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