Forward Deployed Engineer, Applied AI

Snowplow•,

About The Position

Snowplow is hiring its first Forward Deployed Engineer on the Applied AI team, which leads the company’s AI-native transformation. Reporting to the Senior Director of Applied AI, you will work with one business function at a time, partnering with its leader and employees to understand how work is performed, build agentic workflows, refine them with domain experts, and hand them back to the function to operate. Engagement priorities are selected by the Senior Director and the function leader and may shift as the transformation progresses. The role combines software engineering with workflow discovery. You will map current processes, provide evidence about whether AI is appropriate, build compatible agentic workflows on Snowplow’s shared factory framework, establish approval gates and records of agent activity, and prepare the function’s knowledge, systems, and quality standards for agent use. You will focus on reliability, edge cases, evaluation, and adoption after prototypes are built, then train the function to maintain and evolve the workflow. After handoff, you will coordinate improvements with embedded and platform engineers and contribute reusable patterns and reference implementations to the shared framework. The position reports to the Senior Director of Applied AI.

Requirements

Fluency in at least one of Python, TypeScript, or Go, with the ability to learn and use the others as needed.
Experience building and shipping software in another organization’s unfamiliar codebase, dataset, or business system.
Experience building LLM-based agents or agentic workflows for real production work, including prompts, tools, evaluation, and failure handling.
An AI-native workflow, automation, or agent that you built and still use, which you can demonstrate during interviews.
Ability to work with non-engineers to understand actual business processes, including undocumented steps; active listening skills.
Self-direction and comfort with ambiguity, changing priorities, and planning work within time-boxed engagements.
A preference for handing off lasting capabilities rather than becoming a team’s ongoing dependency.
Low ego and willingness to replace or retire your own work when a better approach emerges.
Clear communication with senior stakeholders, including the ability to explain workflow changes in a function leader’s terms and uphold important patterns.
A track record in a forward deployed, solutions engineering, internal tools, or consulting engineering role, or experience transforming a process end to end within a company.

Responsibilities

Map how a function’s work operates in practice, including inputs, process steps, approvals, and quality criteria.
Use workflow evidence to assess and explain whether a process is a suitable candidate for agent-based automation, including when AI is not needed.
Design and build agentic workflows using Snowplow’s shared factory framework.
Establish where agents may act autonomously, where human approval is required, and what activity and reasoning should be recorded.
Make function knowledge, systems of record, and quality standards explicit enough for agents to use.
Improve prototypes through reliability work, edge-case handling, evaluation, and collaboration with domain experts to encode their judgment into checks.
Train and hand off workflows to the relevant function or embedded Applied AI engineer so the team can operate and evolve them independently.
Coordinate post-handoff workflow improvements with embedded and platform engineers rather than taking ownership back.
Document the workflow process and contribute reusable patterns and reference implementations to the shared framework.
Communicate with function leaders and other senior stakeholders about changes to their teams’ work and advocate for sound engineering patterns.
Demonstrate an AI-assisted working process on a real task during the interview process.