Principal Applied AI Engineer, Finance

Genesys empowers organizations of all sizes to improve loyalty and business outcomes by creating the best experiences for their customers and employees. They are seeking a Principal Applied AI Engineer to lead the design and delivery of AI and predictive models that transform financial decision-making at scale, focusing on advanced machine learning and software engineering.

Responsibilities:

• 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
• 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
• 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)
• 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
• 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

Requirements:

• 8+ years of experience in data science, software engineering, and AI engineering, with significant experience deploying production systems
• Proven track record of building production AI systems used at scale
• Deep expertise in predictive modeling, including time series forecasting and customer churn modeling
• Advanced proficiency in Python and strong experience with ML/AI frameworks and system design
• Hands-on experience with LLMs, including prompt engineering, fine-tuning, and evaluation techniques
• Strong experience with cloud platforms (preferably AWS), distributed systems, and MLOps practices
• Experience working with financial data and compliance-aware modeling
• Strong software engineering foundation, including API development, containerization (Docker/Kubernetes), and CI/CD pipelines
• Expertise in building production agentic AI frameworks, including multi-agent orchestration, tool-using agents, and autonomous workflows
• Experience building RAG-based systems, vector databases, and semantic search architectures
• Demonstrated ability to lead large-scale AI initiatives and influence technical strategy
• Deep understanding of responsible AI practices, including model alignment, guardrails, and bias mitigation
• Exceptional communication skills, with the ability to translate complex technical concepts into business value
• Track record of mentoring and elevating technical teams in high-impact environments