Principal Applied AI Engineer, Finance

About the role

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.

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.

Qualifications

• 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.
• 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

• 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.

What Sets You Apart

• 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.