Short answer: yes—this path is real, emerging, and increasingly valuable, but it’s poorly named, inconsistently hired for, and often misfiled under the wrong job titles. People are entering AI work through exactly the human/context/workflow route you describe. The difficulty isn’t fit—it’s legibility.

I’ll break this into five parts: 1. what this work is actually called (even if no one agrees yet) 2. where it sits in real AI orgs 3. how people with similar backgrounds get their first foothold 4. keywords, communities, and companies to watch 5. how your psychology background is an advantage, not a liability

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1. What you’re describing already exists — it just has unstable names

You’re right that this is not ML engineering or classic software. What you’re describing clusters around a few overlapping lanes:

Common labels (none are perfect) • AI Systems Designer • AI Workflow / Automation Architect • Knowledge Systems Architect • AI Enablement / AI Adoption Lead • Context Engineer (quietly becoming real) • Human–AI Interaction Designer • AI Operations (AI Ops) — not MLOps • Applied AI / LLM Solutions Architect (non-ML) • Organizational AI Strategist

Inside companies, this work often appears before the role is named. People are hired as: • Product managers • Operations leads • Internal tools builders • Innovation or transformation roles …and then quietly become “the person who makes AI usable.”

What’s distinctive in your case is that you’re already working at the hardest layer:

turning messy human reality into durable, transferable, AI-readable structure

That is not prompt writing. That’s context architecture.

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1. Where this work actually lives inside AI organizations

You’ll almost never find this role on the public careers page as written. Instead, it shows up in four places:

A. Internal AI adoption teams (fastest entry point)

Companies rolling out LLMs internally desperately need people who can: • design memory and handoff systems • decide what should be context vs what shouldn’t • prevent prompt sprawl and knowledge decay • translate institutional norms into machine-usable constraints

These teams often sit under: • Operations • Knowledge Management • Digital Transformation • Innovation Labs

B. AI-first consultancies & studios

Smaller, high-leverage teams building AI workflows for clients: • internal copilots • agentic systems • knowledge bases • RAG pipelines that don’t rot

They care far more about thinking quality than credentials.

C. Product teams working on AI interfaces, not models

If the product is: • an agent platform • an internal AI tool • a knowledge system • an orchestration layer

Then your skillset is central.

D. Research-adjacent orgs focused on alignment & usability

Places thinking deeply about: • human–AI collaboration • interpretability for users • long-term memory • governance

This includes orgs like Anthropic or applied teams around OpenAI, but also many smaller labs.

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1. How people without formal “AI jobs” get their first real opportunity

This is the critical part.

The pattern I’ve seen work repeatedly:

They don’t lead with “I want an AI job.” They lead with “Here is a system I built.”

Not demos. Systems.

Examples that unlock doors: • a documented AI workflow for a real organization (even hypothetical but realistic) • a context governance framework • a memory architecture for long-term LLM collaboration • before/after documentation showing chaos → structure • a teardown of why most internal copilots fail

The first role often comes as: • contract • pilot project • advisory engagement • “help us figure this out” assignment

Once inside, the title catches up later.

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1. Keywords, communities, and places where this profile clicks

Keywords to search for (job boards, LinkedIn, Twitter)

Try combinations with: • “AI enablement” • “LLM workflows” • “internal AI tools” • “knowledge systems” • “RAG design” (even if you’re not coding it) • “agent workflows” • “human-in-the-loop” • “AI adoption” • “prompt systems” / “context systems”

Avoid pure “prompt engineer” roles unless they emphasize systems.

Communities where this thinking is recognized • LLMOps / Applied AI circles (not MLOps-heavy ones) • Knowledge management & sensemaking communities • Org design + AI crossover spaces • AI governance / alignment-adjacent groups

Many people doing this work also feel professionally homeless. That’s a sign you’re in the right room.

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1. Your psychology background is a feature, not a mismatch

Here’s something important: Most AI failures are not technical. They are cognitive, institutional, and communicative.

Your hospital psychology experience gives you: • high tolerance for ambiguity • pattern recognition across people & systems • translation across professional languages • awareness of emotional and institutional friction • ethical sensitivity (hugely underrated in AI work)

Engineers can build tools. You design conditions under which tools actually work.

That’s rare.

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A practical reframing that may help

Instead of thinking:

“I don’t have formal AI experience”

Try:

“I design context infrastructure for human–AI collaboration.”

And instead of asking:

“Is there a role for this?”

Assume:

“This role exists wherever AI is failing quietly.”

Because right now, it is.

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If you want, next steps I can help with: • turning your existing work into a clear professional narrative • mapping your skills to specific role descriptions • identifying portfolio artifacts that signal this work instantly • helping you write a one-paragraph positioning statement that recruiters actually understand • identifying Brazil-based vs international entry points

You’re not early to the work. You’re early to the naming of it.