Dec '25 (edited) • 💬 Family Chat
🕵🏾‍♂️ Translating an AI Architect Job Description
Hey family,
I translated an interesting job description so you all are aware of what you’re really being asked to do in this role.
I rewrote the LinkedIn job description in plain English with zero corporate doublespeak.
Read this to understand:
• what the job actually is
• what skills really matter
• whether your current experience + portfolio line up
This isn’t to scare you — it’s to help you apply intentionally.
---
đź“š AI STRATEGY & ARCHITECTURE
Original:
“Spearhead the design and implementation of transformative AI initiatives leveraging LLMs, Generative AI, and Machine Learning to modernize core business operations.”
✔️ Real Talk:
This means you are responsible for figuring out where AI can replace slow, manual work inside the company.
Not research.
Not innovation theater.
Not “cool demos.”
Your job is to:
- Look at operations
- Identify bottlenecks
- Use existing AI tools to speed things up and reduce human effort
“Transformative” = measurable cost, time, or throughput improvements.
Original:
“Build and maintain scalable AI infrastructure for chatbots, specialized assistants, and administrative domains (Azure, Google Cloud).”
✔️ Real Talk:
You’re setting up internal ChatGPT-style systems and keeping them running.
That includes:
- Deploying them in the cloud
- Making sure they don’t crash
- Making sure usage doesn’t explode costs
- Making sure they connect to internal tools and data
This is infrastructure work, not product design.
Original:
“Design and implement agentic workflows and multi-agent orchestration systems to automate critical processes and decision-making.”
✔️ Real Talk:
They want AI systems that can handle multi-step workflows, not just answer questions.
Example:
- Step 1: AI reviews a request
- Step 2: AI checks data
- Step 3: AI routes or recommends an action
- Step 4: Human approves (usually)
Important note:
Most of what you're building will NOT be fully autonomous. Expect human-in-the-loop (aka human approval) systems.
This line sounds more advanced than what most orgs can actually support.
đź“š CONVERSATIONAL AI & INTEGRATION
Original:
“Develop and deploy Voice AI agents and LLM-based conversational systems to streamline employee interactions and internal/external communications.”
✔️ Real Talk:
You’re building:
- Voice Assistants
- Chatbots
- Internal support assistants
The goal is to:
- Reduce call volume
- Reduce repetitive questions
- Reduce dependency on human support teams
“Streamline interactions” = fewer humans answering the same questions all day.
Original:
“Integrate AI capabilities into enterprise communication platforms (e.g., Google Workspace).”
✔️ Real Talk:
You’re making it possible for people to:
- Ask questions about Docs, Sheets, emails, or calendars
- Generate summaries
- Trigger workflows using natural language
This means heavy API work, edge cases, permissions, and cleanup when things break.
đź“š DATA INTEGRITY & COMPLIANCE
Original:
“Establish robust data analysis and quality-assurance agents to ensure integrity of clinical and operational LLM use cases.”
✔️ Real Talk:
This is about preventing AI from doing something dangerous or stupid with sensitive healthcare data.
You’re responsible for:
- Preventing hallucinations
- Preventing PHI leaks
- Making sure AI outputs are trustworthy
This is not optional. This is core to the role.
Original:
“Develop benchmarking, monitoring, and testing frameworks to ensure performance, reliability, and HIPAA compliance.”
✔️ Real Talk:
You will build systems that constantly answer:
- Is the AI behaving correctly?
- Is it accurate?
- Is it fast enough?
- Is it compliant?
- Can we prove all of that in an audit?
Think dashboards, logs, alerts, and reports — not vibes.
đź“š TECHNOLOGY LEADERSHIP
Original:
“Collaborate with clinical, compliance, and administrative leaders to ensure AI solutions drive reduced reliance on manual processes and improved efficiency.”
✔️ Real Talk:
You will spend a lot of time in meetings.
Your role is to:
- Explain what AI can and cannot do
- Push back on unrealistic ideas
- Align automation with legal and operational realities
- Help leadership reduce admin burden without increasing risk
This is a translation role as much as a technical one.
đź“š ETHICS, COMPLIANCE & REPORTING
Original:
“Adhere to all organizational policies and report any violations.”
✔️ Real Talk:
Document everything.
Follow the rules.
Cover yourself.
This is standard healthcare/legal language, but it matters when something goes wrong.
đź“š REQUIRED QUALIFICATIONS (REALITY CHECK)
Original:
“W2 only, no sponsorship.”
✔️ Real Talk:
They want:
- Full-time employment
- No contractors
- American citizen only
This is about risk and data custody, not just cost.
Original:
“Bachelor’s degree in IT, Computer Science, or related field.”
✔️ Real Talk:
HR checkbox.
Your portfolio and experience matter more than the degree.
Original:
“Experience with workflow automation platforms (Workato, Make, n8n).”
✔️ Real Talk:
They want someone who:
- Knows how to move fast
- Can automate without rebuilding everything from scratch
- Understands real business workflows
This is a very real requirement, not fluff.
Original:
“Proficiency with Microsoft Office and Google Workspace.”
✔️ Real Talk:
You will:
- Write documentation
- Present to leadership
- Track decisions
- Produce compliance artifacts
This is not junior busywork — it’s governance work.
đź“š TECHNICAL REQUIREMENTS (WHAT ACTUALLY MATTERS)
Original:
“Deep technical proficiency in LLMs and RAG architectures.”
✔️ Real Talk:
You must know:
- How RAG works
- How to chunk data
- How to evaluate retrieval quality
- How to reduce hallucinations
- How to manage context over time
Not theory — production understanding.
Original:
“Experience with Azure, Google Cloud, AWS.”
✔️ Real Talk:
They are Azure-heavy.
Google Cloud is secondary.
AWS is legacy or occasional.
Cloud cost management matters.
Original:
“Python and C#.”
✔️ Real Talk:
Python is the fundamental requirement, as you are more than likely writing prompts and/or prompt files in Python.
C# is for integrating with existing enterprise systems.
Original:
“Event-driven systems, APIs, message buses, databases.”
✔️ Real Talk:
You are building real systems, not scripts.
Expect:
- APIs
- Queues
- Async workflows
- SQL + NoSQL
- Vector databases
🤔 REALITY CHECK FOR THIS ROLE
Someone who:
- has built real automations
- understands workflows end-to-end
- has integrated tools using low-code platforms
- has dealt with real users and real constraints
can grow into this role, even if:
- their Python is not yet senior-level
- they rely on AI to assist with code
- they don’t have formal ML credentials
That said…
AI can assist with Python, but it cannot replace system understanding.
The people who succeed are not “prompt engineers who ask ChatGPT for code.” They are system designers who use AI as a force multiplier.
While AI can help you write Python, you still need to understand Python well enough to read it, debug it, and reason about how systems behave in production.
To be successful in this role, you need a portfolio that demonstrates working knowledge of:
- Python and APIs
- Workflow automation tools (low-code or custom)
- System architecture and data flow
- Experience design and human-in-the-loop thinking
- Prompt engineering for reliability (not vibes)
- Clear communication and writing skills
You also need to be:
- Organized and self-directed
- Curious and willing to learn fast
- Comfortable asking for help when needed
- Someone who uses AI as a multiplier, not a crutch
That’s what the market is actually asking for.
🎄 Merry Christmas to those who celebrate.
🎄 Happy Holidays to those who don't
🤖 Build something cool regardless!
Ev
3
2 comments
🕵🏾‍♂️ Translating an AI Architect Job Description
skool.com/uxd-academy
Learn how to create websites, apps, and AI experiences in the real world at your own pace.
Leaderboard (30-day)
1
+4
2
+2
3
+2
4
+2
5
+2
Powered by