Forward Deployed Engineer (MTV)

Intuit Enterprise Suite is how the world's fastest-growing mid-market and enterprise finance teams run their books, close their quarter, and put AI agents to work on real accounting operations — not demos. Forward Deployed Engineers (FDEs) are the team that makes that real: embedded with our customers, shipping production automation, and turning “AI pilot” into “AI in the general ledger.”

This isn't a traditional sales engineering role, and it isn't a traditional product engineering role either. FDEs report into Engineering, work shoulder-to-shoulder with Sales and Account Management on the deals and renewals that matter most, and own the last mile between what Core Engineering ships and what a specific customer actually runs. You'll sit at the intersection of engineering, applied AI, and enterprise finance, and you'll be judged on one thing: did the customer's business measurably change because you were in the room.

Responsibilities

What you'll do

Pre-sale: build the proof that wins the deal

• Get hands-on with prospects before there's a contract: build a working proof-of-value directly in (or against a realistic replica of) the prospect's own data and systems — real integrations and real output, not a scripted demo.
• Partner with Account Executives from first technical touch: run discovery with the prospect's finance and IT teams to understand their chart of accounts, systems, and process gaps well enough to build against them.
• Lead the technical evaluation: architecture and security/compliance reviews, integration feasibility, and technical RFP responses with the prospect's IT and controllership teams.
• Build the technical narrative and ROI case alongside Sales, and present it directly to economic buyers — CFOs and Controllers — as part of the deal cycle. Your proof-of-value is very often what gets the deal signed.
• Flag technical or data-readiness risk early, so Sales scopes and prices the deal accurately and nothing you built pre-sale becomes a surprise post-signature.

Post-sale: architect and ship in customer specific production

• Architect and deploy customer-specific configurations of AI Agents that automate complex accounting processes — strengthening internal controls, closing books faster, and removing manual work from real customer environments.
• Perform deep technical troubleshooting: review and edit Python, write SQL for integrations, and diagnose configuration issues across customer systems.
• Serve as the functional and technical escalation point for issues involving integrations, data flows, and automation logic — including reviewing and optimizing SQL-based validations and API-driven workflows.
• Maintain SLAs for response and resolution; document issues comprehensively and escalate to Engineering with complete context.

Post-sale: own the customer relationship and drive expansion

• Lead high-impact transformation engagements with enterprise finance teams — diagnosing operational bottlenecks and identifying where AI Agents create the most leverage.
• Act as the primary technical point of contact across implementations, engaging directly with CFOs, Controllers, and accounting leaders.
• Guide and train customer teams through workflow redesign, change management, and the value realization required to scale AI Agent adoption across their organization — and carry that same proof-of-value discipline into expansion and renewal conversations.
• Travel to customer sites as needed (up to 30-40%) for pre-sale technical evaluation, hands-on deployment, training, and adoption workshops.

Ongoing: shape the product and the playbook

• Be the Voice of the Customer: surface technical feedback, integration requirements, and product gaps to Product and Core Engineering — as input to their roadmap, not a substitute for it.
• Partner with Product Management on learnings from onsite discovery sessions to design workflows centered on AI Agent deployment and business outcomes.
• Build the playbook: create implementation guides, expansion frameworks, and reusable assets so every deployment makes the next one faster.
• Educate customers and internal teams on the strategic value of AI-enabled workflow transformation and best practices for building durable AI Agents.

Where this role sits

FDEs report into Engineering, not Sales — but this is very much a field role, spanning both sides of the signature, and the two partnerships below are the job as much as the code is.

Your partnership with Sales & Account Management

• You're the technical face of Enterprise Suite in the field, on both sides of the deal: Account Executives pull you in pre-sale to build the proof-of-value that wins the technical evaluation, and Customer Success pulls you in post-sale for QBRs, renewals, and expansion.
• Deals and renewals in this business run on proof, not promises — you're the one who produces it, first as a working pilot in the sales cycle, then as a live production system afterward. That makes you directly responsible for de-risking the pipeline, even though you don't carry a number yourself.
• You'll work daily with Account Executives and Customer Success on the accounts you're assigned to; you won't be measured on bookings, but expect to be in the room for the technical parts of both the sale and the renewal.

Your partnership with Core Engineering

• You build on top of what Core Engineering ships — you're not a parallel path to the platform roadmap, and this role doesn't re-architect the core product.
• Architect and deploy customer-specific configurations of AI Agents that automate complex accounting processes — strengthening internal controls, closing books faster, and removing manual work from real customer environments. Candidates should have hands-on experience with agentic AI workflows (using, configuring, or building agents/tools)
• Your job is the last-mile layer: wiring existing AI Agent capabilities into one customer's specific data, workflows, and edge cases, fast enough to prove value inside a live deal.
• In return, you feed Core Engineering structured signal — the patterns that show up across multiple customers — so they can decide what to productize. FDE work should make the core roadmap sharper, never duplicate it or bypass it.

Qualifications

What you'll bring

Required

• 5 to10+ years in Forward Deployed Engineering, Solutions Engineering, Sales Engineering, technical consulting, or a comparable hands-on, customer-embedded engineering role (scope and level matched to experience — see Levels & Compensation below).
• Deep experience with Agentic AI: LangChain, LangGraph, MCP servers, multi-agent, RAG, vector stores, hybrid retrieval
• Experience with LLMs & tooling: OpenAI, Gemini, Anthropic; GitHub Copilot, Gemini Code Assist; prompt engineering
• Deep experience with REST APIs, GraphQL, webhooks, SQL, and enterprise integrations.
• Strong technical fluency and the ability to collaborate deeply with Product Managers and Engineers as a peer, not a hand-off.
• A track record of managing multiple technical deployments simultaneously under delivery pressure.
• Comfort leading technical troubleshooting calls with customers and internal IT teams.
• Comfortable operating on both sides of the signature — building a credible proof-of-value under sales-cycle time pressure, and running that same system reliably in production afterward.
• You default to automation — you use AI and modern tooling to move faster, document better, and scale your own impact.
• Demonstrated ability to translate ambiguity into executable requirements, and to communicate insights clearly to both engineers and executives.
• Strong commercial awareness, stakeholder management, and the ability to operate effectively under delivery and revenue pressure.

Preferred

• Background in high-growth SaaS, applied AI, or fintech/ERP/accounting-adjacent domains.

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