Principal Applied AI Engineer, Finance

Be the one building AI-powered experiences where they matter most.   At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships.   Help build, support and operate technology used by more than 8,000 organizations in over 100 countries – moving AI from possibility to production in real-world enterprise environments every day. 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.

Agentic AI & Generative Systems Architect and lead the development of agentic AI systems that automate and augment finance workflows (e.g., forecasting, reporting, and decision support). Design and implement multi-agent systems leveraging LLMs, tool-use frameworks, and orchestration patterns (e.g., RAG, model chaining, dynamic prompting). Translate cutting-edge research in LLMs and agentic AI into scalable, production-ready solutions. Establish guardrails, evaluation frameworks, and responsible AI practices to ensure safe, compliant, and reliable outputs. Design fault-tolerant, observable agent systems with clear failure modes and recovery strategies Predictive Modeling & Customer Behavior Forecasting Lead the design and implementation of advanced predictive models, including time series forecasting and attrition prediction across customer segments. Develop interpretable, production-grade models that drive retention strategies and financial planning. Define and standardize evaluation metrics, validation frameworks, and monitoring systems for model performance and drift detection. Translate complex predictive insights into actionable recommendations for finance and business leaders. Software Engineering & AI System Architecture Design and build scalable AI/ML systems with a strong emphasis on software engineering best practices (modular design, APIs, CI/CD, testing). Lead end-to-end development from concept to production, ensuring robustness, scalability, and maintainability. Develop and integrate AI services into internal applications and workflows, including light front-end/back-end components where needed. Drive adoption of modern tooling (e.g., containerization, orchestration, cloud-native architectures). Operationalization & Model Lifecycle Leadership Establish and enforce MLOps best practices for deployment, monitoring, retraining, and governance of AI systems. Ensure systems meet enterprise standards for security, compliance (e.g., SOX), and auditability. Develop advanced feature engineering strategies capturing behavioral, financial, and temporal signals. Technical Leadership & Strategy Set technical direction for AI/ML initiatives across the finance organization. Lead complex, cross-functional projects and mentor other data specialists. Work alongside stakeholders across finance, IT, and product to adopt AI-driven solutions. Contribute to long-term AI strategy, identifying opportunities to drive efficiency and innovation. Key Qualifications 8+ years of experience in data science, software engineering, and AI