Forward Development Engineer

CrossCountry Consulting is an advisory firm providing accounting and risk, technology-enabled transformation, and transaction solutions. The Forward Development Engineer will design, deploy, and manage production-grade agentic AI solutions for finance and enterprise functions, working directly with clients to build integrations, workflows, and reliable AI systems. The role also includes developing reusable accelerators, mentoring junior engineers, and contributing to practice leadership and business development.

Responsibilities:

• Rapid Jumpstart deployment: Deploy AI Jumpstart packages within client environments, configuring data connectors, calibrating agent logic, and validating outputs against source system controls within the first week of engagement
• Agentic workflow design & build: Architect and implement multi-agent orchestration workflows using frameworks such as LangGraph, CrewAI, or AutoGen; design task decomposition, inter-agent messaging, tool-call schemas, and human-in-the-loop (HITL) / human-on-the-loop (HOTL) checkpoints appropriate to the risk and materiality of each workflow step
• LLM integration & prompt engineering: Write, version, and optimize system prompts and structured output schemas for LLM-powered agents; implement function-calling / tool-use patterns against financial APIs; and fine-tune retrieval strategies (hybrid structured + RAG) to maximize accuracy and auditability
• Enterprise system integration: Build and certify MCP connectors and REST/GraphQL integrations to ERP (NetSuite, SAP, Oracle), EPM (Adaptive Planning, Anaplan), CRM (Salesforce), and HRIS (Workday) systems, ensuring data freshness, completeness, and reconciliation to source-system controls
• Production deployment & reliability: Package agents as containerized microservices (Docker/Kubernetes or equivalent) and configure CI/CD pipelines, environment promotion (dev → staging → production), and monitoring/alerting so agents run reliably at client scale
• Client co-development: Work shoulder-to-shoulder with client finance, IT, and data teams; translate business requirements into technical agent design; facilitate working sessions; demonstrate working agents to executive stakeholders; and iterate rapidly based on feedback
• SOX & audit-readiness: Instrument agents with deterministic logging, source citations, and control documentation so every agent-generated output can be traced, validated, and presented to internal or external auditors
• Contribute to developing and implementing firm-approved, AI-enabled solutions for clients, in accordance with company policies on data protection, intellectual property, and professional standards
• Stay informed about emerging AI tools and techniques and collaborate with firm leadership to identify compliant opportunities to enhance client solutions and internal processes
• Developing reusable accelerators—contributing battle-tested code, workflow templates, and connector certifications back to the AI practice accelerator library to reduce deployment time on future engagements
• Creating new delivery methodologies and service offerings that scale agentic solutions across clients and enterprise functions
• Mentoring analysts and junior engineers on engagement teams, tracking and directing performance against objectives while encouraging continuous improvement and innovation
• Contributing to recruiting, proposal writing, and firm-wide AI innovation initiatives

Requirements:

• 5+ years of software engineering experience with at least 2 years focused on AI/LLM application development, agentic systems, or intelligent automation
• Hands-on production experience building multi-agent systems with LangChain/LangGraph, CrewAI, AutoGen, or equivalent frameworks; understanding of agent-loop design, task planning, tool-use, and memory management
• Proficiency in Python (primary) and TypeScript/JavaScript; experience with FastAPI or equivalent for exposing agent capabilities as APIs
• Deep understanding of LLM capabilities and limitations: prompt engineering, structured outputs, function calling, context-window management, cost optimization, and latency tradeoffs across frontier models
• Experience integrating enterprise source systems via APIs and MCP connectors; familiarity with ERP, EPM, EDW/Data Lakes, and CRM data schemas in a finance context is a significant plus
• Working knowledge of cloud data platforms (Azure, GCP, or AWS) and containerization (Docker, Kubernetes); experience with vector databases (Pinecone, Weaviate, or equivalent) and RAG pipelines
• Finance domain fluency—ability to understand and implement workflows covering month-end close, variance analysis, revenue recognition, AR/AP, and FP&A without requiring extensive hand-holding from client finance teams
• Exceptional ability to operate in ambiguous, fast-moving client environments; a demonstrated track record of delivering working software in compressed, high-stakes timelines
• Clear, confident communication with both technical and non-technical stakeholders; ability to present live-agent demonstrations to CFOs and finance leadership
• Openness to continuously learning and applying emerging LLM capabilities, agent frameworks, and enterprise integration patterns
• Bachelor's degree from an accredited university in computer science, software engineering, mathematics, or a related technical discipline
• prior “forward deployed” or client embedded engineering experience strongly preferred
• Relevant certifications in cloud platforms (Azure AI Engineer, AWS Machine Learning Specialty, GCP Professional ML Engineer), LangChain, or equivalent agentic AI tooling preferred