Lead Software Engineer, Generative AI

At Varicent , we’re not just transforming the Sales Performance Management (SPM) market—we’re redefining how organizations achieve revenue success. Our cutting-edge SaaS solutions empower revenue leaders globally to design smarter go-to-market strategies, maximize seller performance, and unlock untapped potential. Varicent stands at the forefront of innovation, celebrated as a market leader in the 2025 Forrester Wave Report for SPM , 2023 Ventana Research Revenue Performance Management (RPM) Value Index , Gartner Peer Insights , 2024 Gartner SPM Market Guide , and G2. Our solutions are trusted by a diverse range of global industry leaders like T-Mobile, ServiceNow, Wawanesa Bank, Shaw Industries, Moody's, Stryker and hundreds more. Here’s why you’ll thrive at Varicent :

• Innovate with Purpose: Build impactful solutions for customers worldwide.
• Join Excellence: Work in a diverse, collaborative, and innovative team.
• Shape the Future: Lead in redefining revenue optimization.
• Grow Together: Unlock your potential in a supportive environment.

Join us at Varicent —where your talent and ambition meet limitless opportunities for success!

We’re building production GenAI features and looking for a staff software developer with strong engineering skills and a passion for building scalable AI systems. You’ll contribute to the design and delivery of GenAI features, collaborate closely with technical leads and senior stakeholders, and play a key role in maintaining technical quality and scalability across a feature area.

Own AI-enabled components end-to-end and deliver measurable improvements in quality and reliability. You operate independently, drive design decisions within your area, and help set strong engineering standards through reviews and mentorship.

🔧 What you’ll do

• Own delivery for significant features or systems: clarify requirements, design the solution, execute, ship, and iterate.
• Build and maintain reliable services and workflows that support AI features in production.
• Define how quality is measured for your area (success metrics, test strategy, evaluation approach) and automate it wherever possible.
• Run structured experiments, interpret results, and translate learnings into product improvements.
• Improve operational readiness: deployment automation, monitoring/alerting, incident follow-up, and performance/cost optimizations.
• Mentor teammates through design feedback, code reviews, and practical guidance.

🎯GenAI expectations

• Solid working knowledge of how modern AI applications are built and evaluated (you may not be a specialist, but you understand the core concepts and trade-offs).
• Strong learning speed: can evaluate new models/tools, assess feasibility, and recommend a path forward based on evidence and ROI.
• Builds solutions that are safe and compliant by de