Text and Speech Analyst

Essential Duties and Responsibilities

• Design, develop, and refine automated speech recognition models and contextual text mapping techniques to ensure high-quality outputs for specific use cases.
• Employ techniques to guide and enhance Automated Speech Response and text harvesting models, ensuring that the AI interactions are effective and efficient.
• Develop effective AI text and speech services through proficient programming and utilization of ASR engines using standard and fit-for-purpose frameworks.
• Collaborate with cross-functional teams to ensure that the models are aligned with the business requirements and objectives.
• Conduct testing of variants and analyzing model behaviors, including deviations and degradations within given frameworks.
• Stay up to date with advancements in speech recognition and text harvesting technology and engineering best practices.
• Collaborate with product managers, data scientists, ML engineers, and business stakeholders to integrate AI models into business processes, products, or workflows.
• Create documentation and reusable libraries for internal and external use.
• Partner with the MLOps Engineer and other stakeholders to establish and implement observability and monitoring frameworks to adequately and timely identify degradations and potential ethical/bias issues.
• Establish and implement explainability frameworks.
• Provide recommendations for improvement in the areas of AI acceptable use and ethics, collaborating with Legal and Compliance to ensure adherence.

AI Governance and Security

• Ensure data quality and integrity as they apply to text and speech models.
• Ensure that data security protocols are followed in the definition and implementation of AI solutions in accordance with regulatory requirements and company policies.
• Develop safety filters and guide the ethical use of prompts to prevent biased or harmful responses.

Collaboration and Strategy

• Work closely with IT, products, data engineers, data analysts, data scientists, and business stakeholders to understand needs, data requirements, and implement solutions.
• Provide technical leadership and mentorship to junior data team members.
• Provide training and support to team members on effective frameworks and best practices.
• Stay updated on emerging technologies and best practices, making recommendations for continuous improvement.