Head of Forward Deployed Engineering

Shamrock AI

San Francisco, Northern (CA, KY)

Hybrid

USD 200,000 - 250,000

Full time

11 days ago

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Benefits offered by this job

Full medical, dental, vision

Learning budget

Job summary

Shamrock AI is seeking a pioneering Head of Forward Deployed Engineering to own and scale our FDE function. You will personally deploy strategic enterprise engagements, build the team, playbooks, and operating model that scales them, and work closely with Sales to drive outcomes.

In year one you’ll be hands-on with deployments, then hire senior engineers, define templated scoping, and craft a repeatable delivery playbook from contract to production.

Qualifications

Engineering Leader: 10+ years in technical roles with 4-5 years leading FDE or Solutions Eng teams.
Technical Depth: hands-on and capable debugging pipelines and integration code.
Enterprise Deployer: deployed inside complex enterprise environments with cross-team alignment.
Customer Translator: translate between data engineers and CIOs to drive platform decisions.
Background mapping: experience at enterprise AI, data infra, or LLM platform companies.

Responsibilities

Own end-to-end enterprise deployments embedded with data and engineering teams.
Build the FDE team: hire senior engineers and establish career frameworks.
Design the engagement model: scoping, success metrics, and delivery playbook from contract to production.
Close the feedback loop between field and product, translating deployment patterns into roadmap.
Partner with Sales/GTM to scope engagements and support strategic account closures.
Set technical standards for FDE: integration patterns and code quality.
Own executive relationships at key accounts from CIOs to Chief Data Officers.
Represent Shamrock AI in customer workshops and industry events.

Skills

Engineering leadership

Hands-on debugging

Enterprise deployment

Stakeholder management

Talent hiring

Job description

About the Role

$200,000 - $250,000 USD + Early Stage Equity

You will be the first person to build and own the Forward Deployed Engineering function at Shamrock ai - personally deploying our most strategic enterprise engagements while building the team, playbook, and operating model that scales them.

The Problem You’ll Own:

Large enterprises buy AI. Then they discover their data isn’t ready for it. SAP and Snowflake don’t agree. Vendor feeds are inconsistently formatted. Business rules live in someone’s head. The AI produces outputs no one trusts.

We fix this at the data layer - before the AI ever runs. But deploying that fix inside a complex enterprise requires someone who can map the technical landscape, earn stakeholder trust, and ship integrations that hold in production. That’s what you’ll own.

What You’ll Do:

Owning strategic enterprise deployments end-to-end - embedding with customer data and engineering teams, deploying our platform, and driving measurable outcomes
Building the FDE team: hiring senior engineers, setting the bar, establishing career frameworks
Designing the engagement model: scoping, success metrics, and the delivery playbook from signed contract to production
Closing the feedback loop between the field and product, translating deployment patterns into roadmap decisions
Partnering with Sales and GTM to scope engagements commercially and support strategic account closures

You’ll also:

Set the technical standards for the FDE function - integration patterns, code quality, and how edge cases get resolved
Own executive relationships at key accounts, from CIOs to Chief Data Officers
Represent Shamrock ai in customer workshops, industry conversations, and in front of prospects

What This Is Not:

Not a purely managerial role - you will be hands-on in customer environments, especially in year one
Not a sales role - you are a technical leader who supports sales, not a quota carrier
Not a role where you inherit a team - the team doesn’t exist yet; you are building it
Not for someone who needs structure to be in place before they operate - you create the structure

In Your First 90 Days:

Days 1-30: Embed with active customer deployments. Own one end-to-end. Document every friction point - integration gaps, stakeholder blockers, missing platform features

Days 31-60: Deliver a working deployment at a new account. Write the first FDE playbook: scoping template, integration runbook, success metrics framework

Days 61-90: Make your first hire. Define the interview process and career framework. Present the FDE operating model and 12-month team plan to the founding team

Why Now?

Every enterprise AI project hits the same wall: the data isn’t ready. We’re already solving this in production at a Fortune 100 AI company and expanding into healthcare and financial services. The question is how fast we scale - and that starts with this hire.

Compensation & Logistics

Salary: Competitive with VP / Head-of-level roles at growth-stage AI companies

Equity: Meaningful early-stage equity grant - you are building the function, not joining it

Location: San Francisco; travel required for customer deployments (~15-30%)

Benefits: Full medical, dental, vision; learning budget

Requirements

This role exists at an intersection very few people occupy. You need all four of these:

Engineering Leader: 10+ years in technical roles, with 4-5 years leading FDE or Solutions Engineering teams at an enterprise software or AI company. You have built a function before, not just managed within one

Technical Depth: Hands-on and staying that way. Comfortable debugging pipelines, reviewing integration code, and getting into the weeds when a deployment stalls. Technical credibility is how you earn trust

Enterprise Deployer: You have deployed inside complex enterprise environments - navigating data governance reviews, aligning business units, managing integrations across teams with competing priorities

Customer Translator: You translate between a data engineer debugging a schema mismatch and a CIO making a platform decision. You know which conversation you’re in and how to drive both

Background That Maps Well: Experience at an enterprise AI, LLM platform, or data infrastructure company - Palantir, Cohere, Salesforce AI, Scale AI, Glean, or similar

Built a Forward Deployed Engineering or Solutions Engineering function from 0 to 1 - not just inherited one

Familiarity with enterprise data environments: SAP, Snowflake, Databricks, REST APIs, and the failure modes at their boundaries

Experience defining professional services engagement models: scoping, pricing, capacity planning

Track record of hiring and retaining senior technical talent in competitive markets

Domain exposure to healthcare, financial services, or manufacturing

About the Company

We’re building systems that continuously validate data and business processes across large enterprise environments. Enterprises run on multiple systems: ERP (e.g., SAP), APIs, internal tools, and data platforms (Databricks, Snowflake, Postgres). Inconsistencies in data - either from external vendors, internal processes, or data migrations break workflows. When AI is layered on top, those failures scale.

We build the layer that: