Building AI systems for business owners to do less and scale better using my bespoke AIM philosophy that improves productivity + top line dollars.

- I’m a data and AI platform architect who designs and delivers production systems end-to-end (architecture + hands-on build + cross-functional execution).
- Recent focus: MLOps, GenAI/RAG, data pipelines, governance, observability, and cost/performance tradeoffs.
- I typically help teams:
  - Stand up data platforms (batch/stream, lakehouse/warehouse, SLAs)
  - Deploy ML/LLM workflows (evaluation, monitoring, rollback, security)
  - Align stakeholders and deliver roadmaps as a program lead / TPM when needed
- Tech: AWS, Azure, Databricks, Spark, Python, CI/CD, Terraform, Kubernetes (only include what you can defend)
- Open to: Data/AI platform architect and TPM (data/AI) roles (contract or FTE)

Governance doesn’t have to slow teams.
I build paved roads instead.

When the safe path is easiest,
teams move faster.

What I try to “pave” first:

---> default data classifications
---> standard access patterns
---> dataset contract templates
---> approved tool allowlists
---> built-in logging and tracing

For example, when a team needs data:
they start from a contract template.
Owner, SLA, and tags are baked in.

Less debate.
Less rework.
More shipping.

What’s the best paved road you’ve seen?

Elia Ahadi

AI/data platform lead | Solution design + delivery | Databricks, governance, trusted metrics

When an AI agent fails,
I don’t call it “hallucination.”

I bucket failures.
So we can fix the right thing.

My simple taxonomy:
--- Retrieval:  wrong or missing source
--- Data freshness:  source is stale
--- Tooling:  API error, timeout, bad schema
--- Permissions:  access denied, missing role
--- Policy:  blocked by rules or constraints
--- Workflow:  unclear handoff or escalation
--- User intent:  vague request, wrong routing

Different bucket.
Different fix.

What bucket shows up most for you?

Most AI agents aren’t “magic AI.”
They’re workflows with clear boundaries.

I think of autonomy as levels:

-> L1 Assist --- drafts and summarizes
-> L2 Recommend --- suggests actions and reasons
-> L3 Execute with approval --- runs tools, waits for sign-off
-> L4 Execute within limits --- acts inside guardrails
-> L5 Self-directed --- plans and completes end-to-end


The product work is the boundary.
Make it explicit and enforced.

Boundaries look like this:

-> permissions and roles
-> tool allowlists and schemas
-> rate limits and spend caps
-> safe fallbacks and escalation


Where do you draw the line today?
Recommend only, or tool execution too?

When I ship an AI agent,
I set the RACI early.

Without it, incidents get messy.
Fast.

Here’s how I usually split it:
1. Product (me): success metrics, scope, rollout
2. Data owner: definitions, freshness, SLAs
3. Engineering: tools, reliability, deployments
4. Risk/compliance: policy, constraints, approvals
5. Ops/on-call: monitoring, comms, runbooks

Then I make 3 things explicit:

1) who approves changes
2) who owns failures
3) who can shut it off

If answers go stale,
I don’t blame the model first.
I check freshness and ownership.

How do you assign ownership today?
Central, domain, or hybrid?

Most data platforms don’t fail because of the stack.
They fail because ownership is unclear.

3 questions I ask early:

Who owns metric definitions?
Who approves breaking schema changes?
Who owns data incidents + SLAs?

If those are fuzzy, trust drops fast, no matter how modern the tooling is.

What’s worked best for you 
-> central, domain, or hybrid?

The MVP nobody wants to build:
a repeatable test suite for your AI agent.

Think “unit tests,” but for AI.
Same tasks, every release.
Same scoring, every time.

My minimum setup:

1 --- 20–50 real “golden” tasks
2 --- clear pass or fail rules
3 --- failure reasons tagged (why it failed)
4 --- cost and latency tracked
5 --- run it before every deploy

This changes everything.
Faster debugging when it breaks.
Safer upgrades over time.


Do you have this kind of test suite?
Or is it still manual today?

Most AI agent outages
look like “model issues.”

They’re usually:
---> retrieval issues
---> tool issues
---> data freshness issues
---> permission issues

Tracing exposes that.
Fast.

What failure shows up most for you?

taplio

Elia Ahadi's Linkedin Analytics

Elia Ahadi

Performance ScoreElia Ahadi's LinkedIn Performance

Elia Ahadi's Best Posts (last 30 days)

Ready to grow your LinkedIn presence?

taplio

.css-6ytdur{background-image:linear-gradient(284deg, rgba(39, 43, 72, 1) 0%, rgba(33, 47, 183, 1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Elia Ahadi's Linkedin Analytics

Elia Ahadi

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Elia Ahadi's LinkedIn Performance

Elia Ahadi's Best Posts .css-1emoo84{font-size:var(--chakra-fontSizes-md);font-weight:400;color:var(--chakra-colors-gray-600);}(last 30 days)

Ready to grow your LinkedIn presence?

Elia Ahadi's Linkedin Analytics

Performance ScoreElia Ahadi's LinkedIn Performance

Elia Ahadi's Best Posts (last 30 days)