AI Architect, Finance

The AI Architect, Finance, is a senior individual contributor and the principal technical designer of AI-driven solutions across the Finance organization. Reporting to the Director, Strategic Transformation, this role will partner closely with the finance organization (FP&A, Accounting, Treasury, and adjacent business teams) to translate complex finance problems into pragmatic, production-grade technical solutions with a strong emphasis on agentic AI, intelligent automation, and machine learning for forecasting and planning.

This is an execution-first role focused on prototyping, building, and launching production-grade solutions that deliver measurable impact for Finance. Beyond direct execution, the AI Architect will help influence technical direction, define architectural standards, and establish new ways of working across the organization.

As part of our global hiring process, we kindly ask that resumes/CVs be submitted in English, as it’s our primary business language across teams and regions.
What you’ll get the opportunity to do:
Architect and build agentic AI systems that automate finance workflows, including close, consolidations, variance analysis, management reporting, and ad-hoc analytical requests.
Design multi-agent orchestration systems (planner, retriever, executor, reviewer) with appropriate guardrails, evaluations, and human-in-the-loop checkpoints.
Identify high-leverage automation opportunities across the finance value chain and translate them into clear architectural blueprints, prototypes, and production roadmaps.
Define standards for prompt engineering, tool integration, RAG, memory, and agent observability to ensure consistency and reliability across solutions.
Partner with Engineering, IT, and Security to product ionize agents — addressing identity, access, secrets management, audit logging, and change control.

AI/ML for FP&A & Forecasting
Design and implement machine learning models for revenue, expense, cash flow, and driver-based forecasting, scenario modeling, and anomaly detection.
Lead the evolution from traditional, spreadsheet-driven forecasting toward predictive and probabilistic approaches, including time-series, hierarchical, and causal modeling techniques.
Embed AI-generated insights and narratives into the FP&A planning cycle, partnering with FP&A leaders to ensure outputs are interpretable, trusted, and actionable.
Establish backtesting, model monitoring, drift detection, and performance benchmarks so that models remain accurate and credible over time.
Champion experimentation: define hypotheses, run controlled pilots, and measure impact on forecast accuracy, cycle time, and decision quality.

Technical Leadership & Influence
Serve as the senior-most AI technical voice within Finance, setting technical direction, reviewing designs, and raising the bar on engineering quality across initiatives.
Provide hands‑on prototyping and reference implementations that de‑risk new ideas and accelerate delivery for partner teams.
Mentor and uplift finance analysts, FP&A partners, and adjacent engineering teams on AI and ML concepts, patterns, and responsible use.
Represent Finance in cross‑functional AI governance forums, contributing to enterprise AI strategy, standards, and reusable platform capabilities.

Responsible AI, Governance & Risk
Establish and enforce responsible AI practices for Finance — including model risk management, bias and fairness reviews, explainability, and human oversight.
Define evaluation frameworks and acceptance criteria for AI outputs used in financial decisions and reporting contexts.
Maintain a clear inventory of finance AI use cases, models, and agents, with documented owners, data lineage, controls, and review cadence.
Stay ahead of evolving AI regulation and external standards relevant to financial services and corporate finance, and translate them into practical internal guidelines.
Maintain deep, current expertise in foundation models, agent frameworks, ML tooling, and the rapidly evolving finance AI ecosystem.
Run structured pilots and proofs-of-concept to evaluate emerging capabilities and translate promising results into a defensible production roadmap.
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