Most companies are experimenting with AI. We are not. We used it for this.

deCAR Partners works with CEOs, operating executives, and investors to redesign the workflows that drive business performance — and then deploy AI-enabled systems to execute them in production. Not strategy decks. Not pilots that get shelved. We redesign, build, and deploy. Our platform, Cybernetika, handles the orchestration, governance, and execution infrastructure. We handle the hard part: getting a complex organization to actually change how it runs.

We are hiring people who want to do that kind of work.

The role

This is a forward-deployed transformation role. You will work directly with enterprise clients — understanding how critical operations actually function, identifying where AI changes the economics or execution quality, redesigning the workflows, and helping deploy the systems that make it real.

You will work with senior leaders who have real accountability for outcomes. You will move between business problems and technical constraints in the same conversation, sometimes in the same sentence.

This is not a strategy role. It is not a software engineering role. It is not AI research.

It is a role for people who want to solve hard, consequential problems and see their work deployed at scale — not written up and handed off.

What this work looks like

Revenue and go-to-market workflows rebuilt so commercial teams move faster and execute with more consistency
Customer service operations redesigned with AI triage, decision support, and governed automation that actually holds up in production
Finance and operational workflows restructured for speed, control, and lower cost
Legacy environments integrated with AI execution systems that leaders can manage, govern, and improve over time
Fragmented human processes turned into governed, AI-enabled workflows with real economic accountability

What you will do

Sit with operating leaders and understand how critical work actually gets done today
Identify where workflow redesign and AI execution can materially shift performance — cost, speed, quality, or all three
Redesign workflows that combine human judgment with AI-enabled execution at the right boundaries
Collaborate with engineers to deploy systems into real enterprise environments
Run working sessions that move organizations from ambiguity to implemented design
Measure what was promised and make sure it is real
Build reusable patterns and playbooks that compound across engagements

Who does well here

Most people who succeed have 3–7 years in one or more of these environments:

Management consulting, strategy, or operational transformation
Product or operations at a strong technology company
Enterprise engineering, systems implementation, or technical delivery
High-agency startup environments where scope was undefined and results were not optional

Some are stronger on business problem-solving. Others are stronger on systems and implementation. Both profiles work. What does not work: people who prefer observation to execution, or advice to accountability.

What we look for

Systems thinking, not task thinking
Clear communication with people who know the business and people who know the code
Comfort operating in ambiguity — before the problem is fully defined, not just after
Good judgment about when to simplify, when to push for clarity, and when to escalate
A practical instinct for how AI changes what is actually possible in an operation
Bias toward deployment over commentary

This is probably not for you if

You want a narrowly scoped role with structured handoffs and clear swim lanes
You are looking for pure strategy or pure engineering, separate from each other
AI interests you intellectually but production execution does not
You would rather be judged on the quality of your analysis than the quality of your outcomes
You want the safety of a large platform before you decide to commit

Why this, why now

The window in enterprise AI is real. Most organizations are sitting on processes that can be fundamentally restructured — faster, cheaper, with better execution quality. The firms that learn to redesign and execute at this layer will build compounding structural advantage. The others will have impressive pilot libraries and flat performance curves.

We are building the methodology, the platform, and the team to operate at that level — and we are doing it in a way that is designed to compound.

If you want to do serious work, be in the room with the people making the decisions, and be held accountable for real outcomes — this is worth a conversation.

How to apply

Send your CV with a short cover note to info@decarpartners.com.

In your note, answer the following three questions. Short answers only — a few sentences each is more than enough. We expect 10-15 mins max effort.

Describe something you changed (not recommended changes for). What was the before state, what did you do, and what did the after state look like?
In your view, what are couple of top reasons enterprise AI initiatives fail to reach production? (reminder to be brief)
This role is forward-deployed, on-site with clients, across business and technical stakeholders, before a problem is fully scoped. What in your background makes that a fit rather than a stretch?

Applications without a note will not be reviewed.

deCAR Partners

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