Technical Principal

Why GM Financial Technology

Innovation isn’t just a talking point at GM Financial, it’s how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We’re committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.

Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.

Responsibilities

About the role: 

Seeking a hands-on Principal Engineer to lead the strategy, design, and production deployment of agentic voice solutions using Genesys within a contact center environment. This role requires AWS expertise, Azure familiarity, telephony experience, and the ability to partner with vendors while building, scaling, and optimizing voice bot solutions for enterprise use. The Principal AI Engineer is also a hands‑on technical leader and thought partner who guides teams in designing, building, and delivering enterprise‑grade Generative AI solutions, including agentic systems, multi‑agent orchestration, and Retrieval‑Augmented Generation architectures. This role partners closely with product, enterprise architecture, security, and engineering leadership to ensure AI capabilities are scalable, reliable, compliant, and aligned with organizational standards. Success requires strong technical judgment, the ability to mentor and grow engineering talent, and a commitment to advancing responsible AI adoption across the enterprise.

In this role, you will:

• Guide and grow an AI engineering team by fostering technical excellence, accountability, continuous learning, and strong architectural decision‑making; support hiring, onboarding, and skills development. 

• Design, build, and deliver production‑ready Generative AI solutions, including agentic and multi‑agent systems, RAG pipelines, orchestration frameworks, APIs, microservices, automated pipelines, and testing, with hands‑on contribution in Python, C#, or Java as needed. 

• Define and standardize AI architecture, patterns, libraries, and development practices, selecting and applying Agentic Voicebots, Telephony, etc. 

• Ensure AI solutions are secure, compliant, and responsible by embedding privacy, data governance, auditability, PII protection, risk mitigation, and model evaluation in collaboration with security, legal, compliance, and data governance teams.