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