About the Role
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:
You’ll build and deploy by;
+ 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:
+ Prevents inconsistent data entry
+ Detects inconsistencies across systems
+ Validates business logic in real time
+ Enables AI-driven workflows to run safely and reliably
We’re already live at a Fortune 100 AI company and launching at Fortune 500 scale companies in healthcare and financial services.