Deployment Strategist - German Speaking

About Happy Robot

Happy Robot is the infrastructure for enterprises to build and orchestrate AI workforces.

Our AI workers don't just communicate through voice and email - they make decisions, take action, and run operations autonomously across entire enterprise systems.

Born in Y Combinator (S23) and backed by a16z, Base10, Prysm Capital and Eurazeo with over $200M raised, we power critical operations for global enterprises worldwide.

Our platform is battle-tested in the most demanding environments, where AI has real consequences.

We started in logistics, built our own voice stack, models, and orchestration layer from the ground up, and are now bringing that infrastructure to every enterprise that runs the real economy.

Learn more about our vision inour manifesto. (https://www. happyrobot. ai/blog/manifesto)

Role Overview

We are looking for a Deployment Strategist to join our team.

In this role, you'll partner with Forward Deployed Engineers (FDEs) across the full deployment lifecycle, from first discovery conversation to live production.

You'll lead use case discovery and scoping with senior customer stakeholders, ship prototypes hands‑on to prove value early, then stay close to the build through delivery to bring the strategic, business‑value lens to every decision.

You're the partner who keeps the work pointed at the highest‑impact outcomes, ready to pick up the keyboard alongside the FDE when the moment calls for it.

We're looking for engineers with strategic instincts: people who can write code, but whose deepest strength is the strategic, opinionated lens they bring across every phase of a deployment - spotting where value lives, scoping it realistically, and partnering with engineers to capture it from prototype to production.

At Happy Robot, there's no role seniority or hierarchy. We operate as a team of builders. We execute, we build, we own, and we care deeply about our customers and their success.

What You'll do

• Strategic Leadership and
• Use
• Case

Discovery: Partner with Global and Regional C-Level executives and VPs to assess AI opportunities and prioritize the highest‑impact use cases.

Lead curiosity‑driven discovery that surfaces the workflows where AI creates the most business value, well beyond what the customer thought to ask for.

Build the trusted relationships that let you present findings and challenge assumptions at the most senior levels, keeping technical delivery aligned with their business priorities.

• Scoping: Frame impact versus effort with rigor on both axes.

On business value, pressure‑test the realistic upside, question whether the use case as posed will actually deliver the most impact, and explore whether reframing it or pivoting to an adjacent process would unlock more.

The art is deeply understanding how to maximize value of this use case for the customer, not just delivering what was asked for.

On technical effort, map integrations, data, and platform constraints, and surface the edge cases and dependencies that shape timelines.

Land on a plan that's realistic on both.

• Prototyping: Build prototypes and demos hands‑on.

Use our platform and AI‑powered coding tools to test ideas end‑to‑end at light scope.

Prove value early, before the customer commits.

• Program

Management: Coordinate work across the active deployments in an account.

Be the escalation point for the customer and the watchout partner for the FDE.

Stay close enough to the build that you both understand the use case deeply, spot opportunities to capture more value, and catch when work is drifting from the highest‑impact path.

• Prioritization: Sequence use cases within an account so each deployment compounds the next.

Make the call on what gets built when, what gets parked, and where to invest discovery and building time.

• Customer

Partnership: Be the main business‑side point of contact for the account.

Build trust with operational and executive stakeholders day‑to‑day.

Translate AI capabilities into language they can act on.

• Onsite Embedding: Travel onsite for kickoffs, mapping sessions, and key moments where being on the ground changes the outcome.
• Product Feedback Loop: Bring field signal back to Product and Engineering. Identify reusable patterns, common edge cases, and where the platform needs to evolve.
• Must Have
• Background that mixes technical depth with strategic instinct, and a real history of building things.

Common paths include engineering (Computer Science, electrical, industrial, systems), data science, founding a tech startup, product management, or technical / strategic consulting.

• Comfortable coding -mostly Python- using LLMs and AI‑powered tools like Claude Code to ship prototypes fast.
• Working understanding of APIs, integrations, and how AI agents sit inside enterprise workflows.