Manager - Digital Services (53006)

Citrin Cooperman's Applied AI Practice designs, builds and runs AI agents inside clients' businesses. Those clients are middle-market companies, private equity sponsors and their portfolio companies, and enterprise clients our team has served for years.

As a Forward Deployed Engineer, you own the technical side of an engagement. You work in a pod with a Deployment Strategist, who owns the business case, the scope and the executive relationship. You own the architecture, the code, and whether the system holds up in production.

Most of our clients don't have large IT or data teams. A typical engagement starts with scattered data, an older ERP, a small IT staff, and a CFO who wants to see the controls before an agent touches the general ledger. You leave with a system in production and a client team that can run it.

WHAT YOU'LL BUILD

• Agents for sales, marketing, service, operations, finance and accounting work, connected to clients' ERPs, accounting systems and CRMs
• MCP servers, agent skills and integrations that give models safe access to client systems, written so the next engagement can reuse them
• Data pipelines that prepare client data for AI: you profile, clean, model and connect the sources before an agent depends on them
• Evaluation suites, monitoring and audit trails, so every agent ships with a measured accuracy baseline and every action it takes can be traced and reviewed

DISCOVERY AND DESIGN

• Run technical discovery with your Deployment Strategist: map the client's workflow, systems and data, then turn the business problem into an architecture
• Pick the right stack for each client: Anthropic or OpenAI models, AWS, Azure, GCP, Salesforce Agentforce, or a mix
• Build proofs of concept quickly, and know what it takes to turn one into a production system

DELIVERY AND PRODUCTION

• Own components from architecture through deployment, monitoring and handoff in the client's environment
• Get systems through client security reviews, access controls and data-handling requirements, including at regulated clients
• Train client teams to operate and extend what you deliver

PRACTICE BUILDING

• Turn what works into reference architectures, starter repos, eval templates and playbooks for the rest of the Practice
• Review designs and code from other engineers on the team
• Send field lessons and platform gaps back to the Practice and to our technology partners

What Success Looks Like in the First Year

• Systems in production at clients, each with a measured accuracy baseline and an audit trail
• Reference architectures and starter repos that other engagements reuse
• You meet the billable utilization target and earn strong client satisfaction scores
• Five to eight years of software engineering experience, including at least one production system you can walk us through in detail
• Client-facing technical delivery experience in consulting, professional services or a forward deployed role; this is a requirement
• Deep expertise in at least one platform: Salesforce, AWS, Azure, GCP, or a frontier lab ecosystem (Anthropic or OpenAI APIs, agent SDKs and tooling)
• Production experience with large language models: prompt design, tool use, agents and evaluation, including why an agent failed and what you would change
• Fluency in Python or a strongly typed language, and working SQL
• Experience designing APIs, data models and integrations, beyond using them
• Able to explain technical trade-offs to finance leaders and executives as clearly as to engineers
• Willing to travel about 25% of the time

Preferred Qualifications

• Finance and accounting operations experience: close, AP/AR, FP&A or audit
• Private equity experience: sponsor work, portfolio operations or value-creation plans
• Delivery in regulated industries such as financial services or healthcare
• Middle-market consulting, where clients have small IT teams and fixed budgets
• Certifications from Anthropic (Claude Certified Developer, Claude Certified Architect), Salesforce (Agentforce Specialist, Platform Developer I), AWS, Azure or GCP
• Experience with MCP, agent frameworks (Claude Agent/Messages SDK, OpenAI Agents SDK, LangGraph) or cloud data platforms (Snowflake, Databricks, Microsoft Fabric, Salesforce Data 360)