Computational Linguist, Generative AI - Sr. Associate

JPMorganChase•Wilmington, NC

About The Position

We’re looking for a versatile Computational Linguist to join our team focused on evaluating and supporting Generative and Agentic AI systems. This role combines linguistic expertise, data analysis, and hands-on experimentation with large language models. This role is ideal for someone who can move between qualitative language analysis and quantitative evaluation. You’ll work cross-functionally with Machine Learning Engineers, Analytics team and annotators to design innovative, rigorous, and scalable evaluation processes for LLM-powered workflows.

Requirements

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
Fluent 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

Nice To Haves

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.

Responsibilities

Serve as a subject matter expert, engaging with various stakeholders throughout the product lifecycle, and maintain a strong understanding of the Chase Digital Assistant’s model from both customer and technical perspectives.
Manage, monitor, and evaluate and version the Chase Digital Assistant’s intent and entity taxonomy and the model training; Enforce taxonomy versioning practices to ensure traceability and rollback capability
Work closely on the adaptation to LLM-driven workflows, ensuring seamless integration of LLMs with existing conversational AI architectures and event tracking systems.
Implement and evolve metrics and KPIs across Model Correctness, Customer Experience, AI Assurance, and Business Metrics and ensure evaluation is transparent, repeatable, and release-decision-ready
Maintain established metrics and introduce new guardrail metrics for LLM and generative use cases
Manage full artifact suite for LLM models including descriptions, prompts, evaluation rubrics, LLM-as-judge prompts, guidelines, calibration data, data statistics & reliability measures.
Apply ontology design principles to improve semantic reasoning and data integration aligned to business standards.
Design frameworks to incorporate knowledge graphs within classification and extraction model architectures.
Align knowledge models with RAG pipelines and agent orchestration to enhance AI functionality.
Work with data scientists, software engineers, and business stakeholders to translate requirements into robust solutions.
Identifying optimization opportunities across teams, supporting continuous improvement across model performance, data quality, and feature coverage for improving customer experience.