Principal Applied AI Engineer, Finance
Genesys · Virtual Office
About this role
Genesys is hiring a principal-level Machine Learning Engineer based in Virtual Office. The posting calls out experience with Python, AWS, Kubernetes, Docker. Compensation is listed at $193,600–$340,600 per year.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- principal
- Track
- Tech leadership
- Employment
- Full-time
- Location
- Virtual Office
- Posted
- May 19, 2026
More roles at Genesys
Job description
from Genesys careersGenesys empowers organizations of all sizes to improve loyalty and business outcomes by creating the best experiences for their customers and employees. Through Genesys Cloud, the AI-powered Experience Orchestration platform, organizations can accelerate growth by delivering empathetic, personalized experiences at scale to drive customer loyalty, workforce engagement, efficiency and operational improvements.
We employ more than 6,000 people across the globe who embrace empathy and cultivate collaboration to succeed. And, while we offer great benefits and perks like larger tech companies, our employees have the independence to make a larger impact on the company and take ownership of their work. Join the team and create the future of customer experience together.
Principal Applied AI Engineer, Finance
We are seeking a Principal Applied AI Engineer to lead the design and delivery of next-generation AI and predictive models that transform financial decision-making at scale. This role sits at the intersection of advanced machine learning, agentic AI, and software engineering, with a strong focus on production-grade AI systems, intelligent automation, and predictive modeling.
The ideal candidate is both a strategic technical leader and hands-on builder—capable of architecting complex AI systems with a software engineering mindset, influencing organizational direction, and delivering measurable business impact. You will drive innovation in Generative AI, lead the evolution toward agentic AI systems, and establish best practices across modeling, deployment, and governance in a finance context.