Forward Deployed Engineer

Tenarai Europe; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Tenarai Europe seeks a Forward Deployed Engineer to bridge sales, engineering, and customer success for enterprise AI-enabled financial workflows in production. You’ll design proof-of-value pilots, guide data integrations, and help close complex deals.

You will deploy customer-specific AI Agents, troubleshoot integrations, and support post-sale expansion while travelling up to 30-40% to client sites. This role demands deep technical fluency and strong communication with CFOs and controllers to

Qualifications

• 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).
• 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.

Responsibilities

• Pre-sale: build a working proof-of-value directly in the prospect’s data/systems and present ROI to CFOs and Controllers.
• Lead technical evaluation: architecture, security/compliance reviews, integration feasibility, and RFP responses with prospect IT and controllership teams.
• Post-sale: architect and deploy customer-specific configurations of AI Agents for automated accounting processes and faster closes.
• Troubleshoot: review Python, write SQL for integrations, diagnose configuration issues across customer systems.
• Maintain SLAs and document issues for escalation to Engineering with full context.
• Post-sale: own customer relationships and drive expansion; train customer teams on workflow redesign and AI Agent adoption.
• Travel to customer sites up to 30-40% for pre-sale and deployment work and workshops.
• Shape the product by feeding engineering with customer feedback to inform roadmaps.

Tools

LangChain

LangGraph

MCP servers

Multi-agent

RAG

Vector stores

REST APIs

GraphQL

Job description

Our Enterprise Financial Platform enables mid‑market and enterprise finance teams to run their books, accelerate quarterly closes, and put AI agents to work on live accounting operations. Forward Deployed Engineers (FDEs) report directly to Engineering, partner with Sales and Account Management on high‑stakes deals and renewals, and own the last mile between core platform capabilities and production customer environments.

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 elevate 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 productise. FDE work should make the core roadmap sharper, never duplicate it or bypass it.

Qualifications

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.
• Domain experience in one of our priority verticals — construction (job costing, WIP, retainage), nonprofit (fund accounting, grant and donor compliance), manufacturing (inventory, cost accounting, production workflows), wealth management (client billing, compliance/regulatory reporting, investment accounting), health care (revenue cycle, compliance‑heavy financial operations), franchise (zee governance, zor performance), or field services (job/work‑order costing, scheduling‑linked billing). You don’t need more than one, but depth in any single vertical materially shortens ramp‑up on real customer engagements.
• Non‑traditional paths are a plus: veterans, former management consultants, competitive programmers, and PhDs have historically made excellent FDEs.
• Prior experience embedded at a customer site for a multi‑month deployment (implementation consulting, forward‑deployed, or field engineering background).