The ad says“Familiarity with agent design patterns, evals, or AI observability”
The ad says“Familiarity with agent design patterns, evals, or AI observability”
The ad says“Comfortable using a variety of AI tools like Claude in the build and test loop to move quickly”
The ad says“Comfortable using a variety of AI tools like Claude in the build and test loop to move quickly”
The ad says“Experience with document processing and spreadsheet-heavy workflows”
The ad says“Hands-on experience building with LLMs in practice: writing and refining prompts, evaluating accuracy, and debugging non-deterministic behavior”
The ad says“Hands-on experience building with LLMs in practice: writing and refining prompts, evaluating accuracy, and debugging non-deterministic behavior”
We are looking for a Forward Deployed AI Engineer to join the Team at DataSnipper
You will embed with our customers to build and refine the AI agents that automate their audit and finance workflows, taking them from zero to live in daily use and turning what you learn on the ground into the signal that shapes our product
You sit at the exact point where our agent platform meets real assurance work, directly reducing manual effort and accelerating audit outcomes
This is a hands-on, customer-facing role in a high-caliber team that moves fast and holds a high bar
You will design and build agents, debug their accuracy in front of a customer, make the feasibility calls, and then make sure engineering hears what you saw
The platform is still being shaped, so the role rewards people who are energized by building on something while making it better, not people who need fully stable tooling before they can deliver
Build and earn the credibility with senior stakeholders like top-firm audit partners, engagement leaders, and methodology owners **earning the credibility to influence how an entire practice adopts AI
Design engagement programs that take a customer from zero to 20+ agents embedded directly into finance workflows and driving measurable impact
Run office hours and resolve remaining issues, then hand off agent know-how cleanly to the Customer Success Manager who owns ongoing account health
Build the more complex agents yourself, solving prompting and accuracy challenges against real audit evidence
Assess the feasibility of candidate agents, and make the call on what is achievable within an engagement
Produce robust agent configurations that anticipate edge cases and hold up under real customer use
Operate a tight feedback loop between the customer and our product and engineering teams so issues and feature gaps reach the right people fast
Surface gaps with enough specificity that they can be prioritized rather than lost and work closely with product and engineering on ideation and prioritization
Foster documentation and knowledge sharing across delivery, product, engineering, and GTM teams
Play a founding role in shaping DataSnipper’s professional services offering, translating early deployment learnings into repeatable frameworks, engagement models, and delivery standards as the FDE function grows
Dedicate part of your time working directly with the product team to help design and develop new product capabilities, bringing the customer and deployment perspective into early-stage product thinking
Mentor incoming FDE team members as the function scales, sharing domain knowledge, delivery instincts, and agent-building craft
Preferred experience
Technical literacy with data and documents, enough to configure them correctly and reason about what is going wrong
The ability to do this in front of customers, under time pressure, without losing their confidence when something breaks
Comfortable using a variety of AI tools like Claude in the build and test loop to move quickly
Excellent written and verbal communication, including the ability to make a customer need legible to engineering
Strong technical aptitude and structured thinking, with the judgment to decompose a customer workflow into agents and decide quickly what is feasible and what is not
Bias toward shipping and iterating rather than perfecting in isolation, and willingness to travel to customer sites when an engagement calls for it
Domain expertise in audit and/or finance or strong interest w/ ability to learn
Hands-on experience building with LLMs in practice: writing and refining prompts, evaluating accuracy, and debugging non-deterministic behavior
Familiarity with agent design patterns, evals, or AI observability
Prior Forward Deployed Engineer, solutions engineering, or implementation experience at an applied AI or enterprise software company
Experience with document processing and spreadsheet-heavy workflows
