Senior Consultant CPM (Forward Deployed Engineer)

Accordion•Chicago, NY

4d•Hybrid

About The Position

Accordion is seeking a Forward Deployed Engineer to embed directly with client finance teams and build AI-powered agents and automated workflows that improve how organizations use their existing Enterprise Performance Management (EPM) and Corporate Performance Management (CPM) systems. The role focuses on creating intelligent solutions on top of current financial platforms, rather than implementing the platforms themselves. Examples include FP&A variance analysis agents, close reporting agents, forecasting co-pilots, and automated data reconciliation workflows. The FDE works at the intersection of applied AI, financial domain expertise, and client delivery. They learn each client’s financial architecture, identify high-value automation opportunities, and deliver production-grade AI agents embedded in day-to-day finance operations. The role can be based at any of Accordion’s US office locations and is hybrid, with the flexibility to work remotely two days a week. Candidates should ideally be local to their desired location. The position is not eligible for immigration sponsorship and may require travel to client sites as needed, up to 50–80% depending on the engagement. The annual salary range is $95,000–$140,000 USD, plus benefits and bonus. Compensation depends on factors including location, skills, experience, education, certifications, labor costs, and internal equity. This is a current vacancy the employer intends to fill by February 1, 2027.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, Finance, or a related field.
At least 3 years of software engineering experience, including at least 1 year building applications powered by large language models, AI agents, or intelligent automation.
Strong proficiency in Python and experience with LLM orchestration frameworks such as LangChain, LangGraph, Anthropic API, or OpenAI API, and related tools such as vector databases, embedding models, and prompt engineering.
Working knowledge of financial processes including budgeting, forecasting, financial consolidation, close management, variance analysis, and management reporting.
Solid SQL skills and experience integrating with APIs, data warehouses, ERPs, and financial systems to source and transform data for AI workflows.
Excellent communication skills and ability to work directly with CFOs, controllers, and FP&A leaders, translate their needs into technical solutions, and demonstrate value through rapid iteration.
Willingness to travel to client sites as needed, up to 50–80% depending on engagement.

Nice To Haves

Understanding of financial planning and reporting systems such as Oracle EPM Cloud, OneStream, Anaplan, Planful, or Workday Adaptive Planning.
Experience in consulting or professional services.
Familiarity with ERP systems such as NetSuite, SAP, Oracle EBS, or Microsoft Dynamics, and how financial data flows into EPM/CPM platforms.
Experience building production AI systems with guardrails for regulated or audit-sensitive environments, including explainability, traceability, and human-in-the-loop review.
Cloud infrastructure experience with AWS, Azure, or GCP, and experience deploying and monitoring agent-based applications in production.
Background in financial data modeling, multidimensional databases, or business intelligence tooling.

Responsibilities

Design, build, and deploy AI agents tailored to client financial workflows, including variance analysis, close reporting, and forecasting.
Integrate AI agents with existing EPM/CPM platforms to read from and write back to clients’ systems of record.
Develop automated data reconciliation and validation workflows for period-end close, intercompany eliminations, and management reporting.
Implement retrieval-augmented generation pipelines, tool-use patterns, and multi-step agent architectures grounded in client financial data and business context.
Embed with client finance, FP&A, and accounting teams to understand their EPM/CPM environments, data flows, reporting cadences, and pain points.
Identify and prioritize opportunities for AI and automation to reduce manual work in areas such as variance commentary, flux analysis, board reporting, cash flow forecasting, and data quality assurance.
Translate business requirements into technical solution architectures, defining agent capabilities, data integrations, guardrails, and success metrics with client stakeholders.
Manage the agent development lifecycle on-site, from prototyping and stakeholder demonstrations through iteration, testing, deployment, and post-launch monitoring.
Contribute reusable patterns and components to Accordion’s agent framework and shared component library.
Keep current with LLMs, agent frameworks, and best practices for building reliable, auditable AI systems for financial contexts.
Establish quality standards for financial AI outputs, including accuracy validation, hallucination mitigation, audit trails, and human-in-the-loop checkpoints.
Document solution architectures, integration patterns, and deployment playbooks to support future engagements and team knowledge transfer.