Principal Applied AI Engineer, Finance

Genesys

Principal Applied AI Engineer designing and delivering next-generation AI in finance at Genesys. Leading the development of predictive models and innovative AI systems for financial decision-making.

Posted 4/15/2026full-timeMaine, Massachusetts, Montana, New York, Rhode Island • 🇺🇸 United StatesLead💰 $225,200 - $396,000 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsDockerKubernetesPython

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

Medical, Dental, and Vision Insurance.
Telehealth coverage
Flexible work schedules and work from home opportunities
Development and career growth opportunities
Open Time Off in addition to 10 paid holidays
401(k) matching program
Adoption Assistance
Fertility treatments

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

predictive modelingtime series forecastingcustomer churn modelingPythonML/AI frameworksLLMsprompt engineeringfine-tuningMLOpsfeature engineering

Soft Skills

leadershipmentoringcross-functional collaborationcommunicationstrategic thinking