EET EEDS Sr Language Engineer

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.

Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.

Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations.

At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

EET EEDS Senior Language Engineer will be responsible for building and maintaining machine learning models to identify end user intent for a multi-channel Virtual Assistant.

Key Responsibilities:

• Design & drive the intent architecture for all of Erica for Employees across 5 domains.
• Work within the framework of a vendor based Virtual Assistant (Amelia).
• Manage the ongoing model governance for E4E’s web and voice models per MRM guidelines.
• Primary contact for MRM team and Erica for Employees.
• Lead the Gen AI POC and accountable for getting production approval and use case in production Q4 2025.
• Lead a team of 7 contactors/FTEs.
• Test, validate, and determine if we replace Microsoft Azure’s ASR with Soundhound’s ASR.
• Understand the intent portfolio for NLU across domains (e.g., technology, human resources) and how it maps to conversation design for web, mobile, and voice channels.
• Identify and build appropriate datasets to train and test machine learning models for intent classification and speech recognition.
• Develop tools and telemetry that can measure/monitor accuracy and performance and update the models accordingly throughout development lifecycle.
• Develop disambiguating and error handling strategies as the virtual assistant scales.
• Monitor conversations in the application to identify underperforming content and develop solutions to improve the performance.
• Collaborate with data scientists, product owners, UX researchers, and engineers to build out the “brain” of the virtual assistant.

Required Qualifications:

• Experience with conversational interfaces and natural language processing.
• Experience training machine learning algorithms for data classification and/or speech recognition.
• Experience improving intent recognition of a data classification model.
• Experience with python.
• Unique skillset in computational linguistics and technical experience.
• Familiarity with LLMs.
• Familiarity with using version control technologies such as git, svn, or JIRA.
• Experience in DevOps and Agile methodology.
• Strong analytical and troubleshooting skills.

Skills:

• Automation
• Influence
• Result Orientation
• Stakeholder Management
• Technical Strategy Development
• Application Development
• Architecture
• Business Acumen
• Risk Management
• Solution Design
• Agile Practices
• Analytical Thinking
• Collaboration
• Data Management
• Solution Delivery Process

Shift:

Hours Per Week: