Forward Development Engineer

Role overview

This role places a seasoned engineer directly inside client organizations to design, build, and deploy production-grade agentic AI solutions for finance and operations teams. The position sits at the intersection of software engineering, financial domain expertise, and client partnership, with a focus on delivering measurable enterprise value on compressed timelines. The engineer will be embedded with client finance leadership, including the Office of the CFO, and serve as a senior technical lead within an AI-focused transformation practice.

Responsibilities

- Architect and implement multi-agent orchestration workflows using frameworks such as LangGraph, CrewAI, or AutoGen, including task decomposition, inter-agent messaging, tool-call schemas, and human-in-the-loop checkpoints calibrated to workflow risk. - Deploy AI Jumpstart packages within client environments, configuring data connectors, calibrating agent logic, and validating outputs against source-system controls during the first week of engagement. - Write, version, and optimize system prompts and structured output schemas for LLM-powered agents, implementing function-calling patterns against financial APIs and tuning retrieval strategies that combine structured queries with RAG. - Build and certify integrations to enterprise systems including ERP (NetSuite, SAP, Oracle), EPM (Adaptive Planning, Anaplan), CRM (Salesforce), and HRIS (Workday), ensuring data freshness and reconciliation to source-system controls. - Package and operate agents for production deployment, monitoring reliability and end-to-end performance. - Mentor junior engineers on the practice team and present live-agent demonstrations to CFOs and finance leadership.

Requirements

- Bachelor's degree in computer science, software engineering, mathematics, or a related technical discipline from an accredited university. - Hands-on engineering experience deploying AI or agentic solutions in production, including containerization with Docker and Kubernetes, vector databases such as Pinecone or Weaviate, and RAG pipelines. - Finance domain fluency covering month-end close, variance analysis, revenue recognition, AR/AP, and FP&A workflows, with the ability to operate without extensive hand-holding from client finance teams. - Demonstrated success delivering working software under compressed, high-stakes timelines in ambiguous client environments. - Clear, confident communication with both technical and non-technical stakeholders. - Continuous-learning mindset with openness to adopting emerging LLM capabilities, agent frameworks, and enterprise integration patterns.

Nice to have

- Relevant certifications in cloud platforms (Azure AI Engineer, AWS Machine Learning Specialty, GCP Professional ML Engineer), LangChain, or equivalent agentic AI tooling.

Benefits and work setup

- Comprehensive healthcare coverage including medical, dental, and vision, plus flexible spending accounts and a 401(k) with company matching. - Generous parental and maternity leave policies, technology stipends, and wellness reimbursement programs supporting professional growth and personal well-being. - Remote-first option available with willingness to travel domestically up to 20–40 percent depending on engagement phase; hybrid option requires availability on client site or in office three days per week with two days remote.