Senior finance automation architect

The Senior Finance Automation Architect is a senior individual contributor and the principal technical designer of AI-driven solutions across the Finance organization. Reporting to the Director, Strategic Transformation, the role partners 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.

• 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 productionize agents — addressing identity, access, secrets management, audit logging and change control.

• 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 modelling 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.

• 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.

• 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.
• Curate a portfolio of high‑impact AI use cases for Finance, balancing quick wins with foundational, longer‑horizon investments.

• 10+ years of progressive experience in AI/ML, software engineering or applied data science, including significant time as a senior IC, principal or staff‑level architect.
• Demonstrated experience designing and shipping production AI systems – ideally including agentic AI, LLM‑based applications and ML for forecasting or planning use cases.
• Deep, hands‑on expertise with modern AI tooling – foundation models and APIs, agent frameworks, RAG patterns, vector stores, orchestration tools and ML libraries (e.g., scikit‑learn, PyTorch, TensorFlow, statsmodels, Prophet).
• Strong software engineering fundamentals – Python, APIs, version control, testing, CI/CD, cloud platforms (AWS, Azure or GCP) and modern data stacks.
• Solid working knowledge of finance and FP&A concepts – budgeting, forecasting, variance analysis, driver‑based planning, management reporting and the systems that support them (ERP, EPM/CPM, data warehouse, BI).
• Experience working with or building on top of finance‑relevant data – general ledger, sub‑ledgers, headcount, sales pipeline and operational drivers – with a strong appreciation for data quality and lineage.
• Working understanding of responsible AI, model risk management, security, privacy and audit considerations relevant to enterprise finance.