AI Deployment Lead

Prague / Bratislava / Remote

Elsewhere this role is called a Deployment Strategist. You lead deployments, not people.

Most enterprise AI projects don’t fail on the model. They fail because nobody figured out which workflow to change, who owns it, what “working” actually means, or what to measure. You’ll be the person who does. You’ll embed with our largest customers — telcos, banks, utilities — and find the workflow that’s genuinely worth automating, or the question nobody can answer today because the answer is sitting in 40,000 call recordings. You design the solution with our AI Solution Engineers and stay until adoption is real. This is not account management and it’s not project management. You own the outcome.

About you

3+ years with enterprise customers in a technical, analytical or consulting capacity — hand you a messy process and you return a structure

You can read an org chart and a call flow with equal fluency, and you know which one blocks the project

Enough Python and API literacy to prototype, demo, and hold your own with engineers — you don’t need to be one

You know what data to use to get to a real insight, and why

Operational metrics come naturally to you, whichever department you land in — contact centre, back office, anywhere with volume and a cost per case

You can run a room: discovery with ten stakeholders, onboarding a team who didn’t ask for this, a training that stops people escalating to you

Commercial instinct: comfortable in scoping and pricing conversations, structuring value cases, working alongside sales without becoming sales

Track record of owning ambiguous projects and finishing them

Fluent in Czech/Slovak and English; comfortable presenting to C-level and to a contact centre team lead on the same day

Familiar with LLMs, agent architectures and where they break

Key Responsibilities

✔️ Run discovery inside strategic accounts — map the real workflows, the volumes, the cost, the politics — and identify where AI creates measurable value

✔️ Map what data actually exists — calls, recordings, emails, tickets, process maps — and what could be known from it that isn’t today

✔️ Define the deployment: scope, success metrics, rollout plan, and what we explicitly won’t do — and what we can honestly promise. Deployments die from use cases sold at the edge of what’s deliverable

✔️ Own delivery outcomes end-to-end with the engineering team — time to production, adoption, containment, CSAT, whatever the customer is actually judged on

✔️ Define what we report and to whom: which numbers, at what cadence, and which one decides whether this was worth doing. Agreeing that after go-live is how projects end in an argument

✔️ Run the workshops, onboarding and training that let the customer’s team take over — agent design, testing, monitoring, change management, and reading their own numbers without us

✔️ Present results and next phases to audiences from engineers to the board — and shape how those results are presented: structure, storytelling, the right cut for the agent team, the ops lead and the C-level. Sometimes you build the reporting, sometimes the Solution Engineer does; the thinking is yours either way

✔️ Solve with configuration first. When something genuinely needs to be built, make the case for the roadmap rather than a one-off — and bring the evidence from the field to back it

✔️ Feed patterns across deployments back into product priorities, and open up new use cases and industries as we scale

Why join us?

You own real deployments at real companies, and you’ll see the moment they start working

More use cases across banking, telco and utilities than we can currently take on — you’ll help choose

Nobody knows yet what enterprise AI should look like. Neither do we, which is why this is fun

️ Internal hackathons — non-engineers very much included, and yes, they’ve won

️ Two weekend teambuildings a year, plus the smaller unofficial ones nobody organises but everyone attends

Genuinely good merch, restocked regularly — our clients wear it too

A young team that genuinely likes each other and argues well about the work

Remote and flexible, competitive pay, no drama about either

We’re building scalable platforms for AI-first products. Work onsite or remotely, whatever helps you deliver your best work.

Side by side

AI Solution Engineer (FDE)

AI Deployment Lead

(Deployment Strategist)

Account Lead

(Client Partner)

Owns

That it works

That it matters

That it lasts

Core question

How do we build and run this?

What should we build, and for whom?

Where is this account going, and what is it worth?

Primary output

Configured agents, MCP servers, integrations, analysis pipelines, dashboards, custom extensions

Use case and measurement definition, scope, success metrics, rollout and adoption plan

Account strategy, stakeholder map, value case, proposals and pricing, expansion plan

Spends the day with

Customer engineers, our platform, production logs

Business owners, ops leads, C-level, our Solution Engineers

Decision-makers and C-level, our Deployment Leads, sales

Depth

Deep in the stack — architecture, data, voice, failure modes

Deep in the workflow — process, volumes, cost, what’s worth measuring, org politics

Deep in the account — business, org, politics, commercials, portfolio fit

Often comes from

Backend or integration engineering, data engineering, voice and contact centre platforms

Consulting, business analysis, BI and analytics, contact centre or back-office operations

Enterprise account management, client partnership, consulting, solution sales

Measured by

Time to production, reliability, containment, quality of what’s built

Adoption, business outcome, expansion into the next use case

Retention, recognised customer value, account growth, quality of relationships

Commercial role

None — supports scoping with effort estimates

Shapes scope and pricing alongside sales

Owns proposal, pricing and negotiation for new work

Number of accounts

1–2 deep; more when the work is analysis rather than integration

2–3, embedded

A small portfolio, held for years

When it goes wrong

It’s live but nobody trusts it, or it breaks under load

It’s built beautifully and nobody uses it

The project delivered and the account still churned — or we heard about the problem too late

Reports into

Delivery / Engineering

Product

Commercial