Principal Applied AI Engineer, Finance

Genesys•Paris, MA

About The Position

Genesys empowers organizations of all sizes to improve loyalty and business outcomes by creating the best experiences for their customers and employees. Through Genesys Cloud, the AI-powered Experience Orchestration platform, organizations can accelerate growth by delivering empathetic, personalized experiences at scale to drive customer loyalty, workforce engagement, efficiency and operational improvements. We employ more than 6,000 people across the globe who embrace empathy and cultivate collaboration to succeed. And, while we offer great benefits and perks like larger tech companies, our employees have the independence to make a larger impact on the company and take ownership of their work. Join the team and create the future of customer experience together. 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.

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.

Nice To Haves

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.

Responsibilities

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.