AI Staff Engineer

Position Overview:

The Staff AI Engineer plays a critical role in advancing InvoiceCloud’s generative AI strategy by designing, developing, and deploying large language model (LLM) and AI-driven capabilities that enhance the InvoiceCloud platform and customer experience. This role operates at the intersection of research, engineering, and product impact — leading the end-to-end lifecycle of AI solutions from rapid prototyping through scalable production deployment.

As a technical expert, the Staff AI Engineer partners with Product, Data Science, Data Engineering, and Platform teams to translate business goals into reliable, measurable, and secure AI implementations. The role drives innovation through applied experimentation, defines reusable frameworks and standards for AI integration, and improves efficiency through automation and observability.

Success in this position means delivering real customer and business outcomes — improving platform intelligence, developer velocity, and operational efficiency — while shaping the technical direction for how AI is built and delivered at InvoiceCloud.

Success Profile:

This role is anchored in four of our company’s core competencies. These competencies reflect the mindsets and behaviors that define success in this role. We outline how each competency translates into real-world actions and outcomes specific to this role.

Results Driven

• Leads end-to-end development of AI/ML and generative models that deliver measurable performance, reliability, and ROI.
• Defines technical KPIs for scalability, latency, and accuracy, ensuring all models align with business outcomes.
• Collaborates with engineering leadership to translate strategy into executable AI roadmaps that drive platform innovation.
• Working with AI Product Strategy Lead to engage customers and prospects to refine our go-to-market and ensure product success.

Takes Ownership

• Acts as a domain authority for AI architecture, frameworks, and lifecycle management.
• Partners with cross-functional teams to ensure data governance, compliance, and operational stability of deployed AI systems.
• Proactively resolves blockers, defines governance routines, and ensures disciplined delivery against milestones and quality standards.

Innovative

• Evaluates, prototypes, and integrates emerging AI technologies—such as LLMs, agentic frameworks, and multimodal systems—to strengthen platform capabilities.
• Pioneers automation strategies leveraging MLOps, model monitoring, and self-learning pipelines to optimize performance.
• Uses GenAI tools (e.g., Azure/OpenAI, LangChain, Semantic Kernel) to streamline model development, automate feature generation, and accelerate experimentation cycles.

Customer Centric

• Designs AI-powered solutions that enhance the end-customer experience, improving personalization, engagement, and payment outcomes.
• Collaborates closely with Product and Client Success teams to align AI initiatives with customer needs and feedback loops.
• Advocates for responsible AI—ensuring fairness, transparency, and trust in all deployed solutions. 

Requirements

• 8+ years of software engineering experience, including 3+ years building AI/ML or LLM-powered applications.
• Demonstrated success delivering scalable AI systems in production environments in a SaaS product environment.
• Expertise with Python and one or more of C++/C#; experience with Semantic Kernel, LangChain, Azure OpenAI, and vector databases.
• Familiarity with model evaluation, prompt tuning, and performance optimization.
• Strong cross-functional communication and technical mentorship skills; thrives in a collaborative, high-ownership environment.