Senior Manager, AI​/ML Engineering & Customer Insights - Frisco

Role Summary

Looking for a role where you can lead, mentor, and stay hands‑on with cutting‑edge AI and machine learning technologies? Build a new AI‑powered customer insights capability from the ground up and shape the future of customer experience by establishing and leading a new strategic function focused on transforming customer experience through data‑driven insights and artificial intelligence.

This role sits at the intersection of Engineering, Product, Data, and Customer Experience. It carries responsibility for building capabilities that aggregate and analyze telemetry data, customer feedback, reviews, support signals, and product usage patterns to identify opportunities that improve customer satisfaction, retention, and acquisition.

You will define the vision, architecture, processes, and operating model for leveraging AI/ML and Generative AI technologies to uncover actionable insights  will be both a technical leader and people manager, capable of developing proof‑of‑concepts, building production‑grade solutions, and leading a growing team of engineers and data professionals.

The ideal candidate combines deep software engineering expertise with practical AI/ML experience, strong business acumen, and the ability to influence stakeholders across engineering, product, and executive leadership teams.

Location Note
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This is a hybrid position located in Frisco, TX, requiring regular onsite presence. We are only considering candidates within a commutable distance to the location and are not offering relocation assistance at this time.

• Establish and lead a new AI‑driven Customer Insights function within the engineering organization.
• Define the strategy, roadmap, and technical vision for leveraging AI/ML to improve customer experience and business outcomes.
• Partner with Engineering, Product, Customer Experience, and Leadership teams to identify high‑impact opportunities and prioritize initiatives.
• Build frameworks, processes, and best practices for insight generation, experimentation, and measurement.
• Design and implement AI/ML solutions that aggregate, analyze, and synthesize data from multiple sources including telemetry, application logs, customer reviews, surveys, support interactions, and product feedback.
• Develop machine learning models, algorithms, and analytical frameworks that identify trends, root causes, customer pain points, and product improvement opportunities.
• Leverage LLMs and Generative AI technologies to automate insight generation, sentiment analysis, summarization, classification, and recommendation workflows.
• Build proof‑of‑concepts and rapidly validate emerging AI technologies before scaling them into production environments.
• Develop approaches to measure the effectiveness and business impact of AI‑generated insights.
• Architect and build scalable cloud‑native solutions on AWS and/or Azure environments.
• Develop robust data pipelines and processing frameworks capable of handling large‑scale structured and unstructured datasets.
• Lead the development of APIs, services, and platforms that support customer insight generation and consumption.
• Ensure engineering excellence through modern software development practices, security, reliability, and operational scalability.
• Drive adoption of MLOps and AI platform best practices for model deployment, monitoring, governance, and continuous improvement.
• Build, mentor, and lead a high‑performing team of engineers and data professionals.
• Provide technical guidance, coaching, and career development support.
• Participate in hiring, technical assessments, and team‑building activities as the function expands.
• Present findings, recommendations, and business impacts to senior leadership.
• Translate complex technical analyses into actionable insights for non‑technical stakeholders.
• Influence product roadmaps and engineering priorities through data‑driven recommendations.
• Collaborate closely with global teams and stakeholders to align customer experience initiatives across the organization.

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Machine Learning, or a related field.
• 10–15 years of software engineering…