Forward Deployed Engineer & Agentic Workflow Engineer

People can sit in the following cities:

Washington DC (22102),  Boston(02109)Chicago (60606) Dallas(75001) Irvine (92618) Los Angeles (91367) New York (10019), San Diego (92130), San Francisco ( 94111)with two locations), Seattle (98101)Tampa (33605) and Miami

TOP NON-NEGOTIABLE SKILLS

1. 5+ years of software engineering experience with at least 2 years focused on AI/LLM application development, agentic systems, or intelligent automation; prior “forward deployed” or client embedded engineering experience strongly preferred.
2. 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.
3. Proficiency in Python (primary) and TypeScript/JavaScript; experience with FastAPI or equivalent for exposing agent capabilities as APIs.
4. Deep understanding of LLM capabilities and limitations: prompt engineering, structured outputs, function calling, context-window management, cost optimization, and latency tradeoffs across frontier models.

WHAT WOULD MAKE THIS HIRE STAND OUT

1. Relevant certifications in cloud platforms (Azure AI Engineer, AWS Machine Learning Specialty, Google Cloud Platform Professional ML Engineer), LangChain, or equivalent agentic AI tooling preferred.
2. Bachelor’s degree from an accredited university in computer science, software engineering, mathematics, or a related technical discipline.

Serve as a key leader in the AI Innovation & Transformation practice by:

• 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.