I am a technology leader with 12+ years of experience architecting scalable commerce systems.

I specialize in building global marketplace platforms, migrating legacy commerce environments to modern platforms such as Shopify and applying AI solutions where they deliver measurable outcomes.

I am a 2x Google Cloud Certified Cloud Architect, bringing deep expertise in cloud-native infrastructure design, Kubernetes, and scalable architectures that deliver 99.99% uptime for global commerce operations.

Key achievements:

- Built a global SaaS marketplace platform from the ground up. Architected the transition from monolith to microservices, modernized the stack and implemented a multi-database model.
- Led multiple enterprise migrations to Shopify and other modern commerce platforms, enabling faster go-to-market and lower maintenance costs.
- Designed an AI solution that analyses historical campaign data and predicts the performance of new campaigns, improving marketing decision-making and reducing manual analysis.
- Architected cloud-native infrastructure with Kubernetes, GCP, and CDN services to deliver 99.99% uptime during launches and high-traffic events.
- Directed teams of 50+ engineers, driving delivery across system design, infrastructure, and product scale.
- Mentored teammates on commerce best practices and coding standards and played an active role in recruitment and onboarding to build stronger, future-ready teams.

I combine deep technical expertise with the ability to translate architecture into measurable business outcomes, while also building teams and practices that scale.

𝗡𝗼 𝗼𝗻𝗲 𝗶𝘀 𝗶𝗴𝗻𝗼𝗿𝗶𝗻𝗴 𝘆𝗼𝘂 𝗼𝗻 𝗽𝘂𝗿𝗽𝗼𝘀𝗲.

They just don’t see it.

Leaders promote from their circle.
Not because they are biased,
but because that is the only circle they know.

No one has full visibility into everyone’s impact.
No one can evaluate every contributor impartially at scale.

So decisions get made with incomplete information.

If you are not in the room,
not in the conversation,
not in the narrative,

𝗬𝗼𝘂 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻.

That is why selling yourself is not optional.

→ Share 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀, not just effort.
→ Make impact 𝗼𝗯𝘃𝗶𝗼𝘂𝘀, not assumed.
→ Build visibility beyond your immediate circle.

𝗪𝗮𝗶𝘁𝗶𝗻𝗴 𝘁𝗼 𝗯𝗲 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱 is not professionalism.
It is 𝘄𝗶𝘀𝗵𝗳𝘂𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴.

So yes, do great work.
But also make sure it is seen, understood and remembered.

Because silence is not humility.
𝗜𝘁 𝗶𝘀 𝗼𝗯𝘀𝗰𝘂𝗿𝗶𝘁𝘆.

How do you increase visibility without sounding like a braggart.

#CareerAdvice #CorporateLife #Visibility #Leadership #ProfessionalGrowth

Mayank Awasthi

Commerce Tech Leader | AI & Cloud Architect (GCP) | 12+ Years Turning Ideas Into Scalable, Shippable Products

𝗜𝘁 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗮 𝗺𝗼𝗺𝗲𝗻𝘁 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗳𝗲𝗲𝗹 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿.

I still remember the first time this hit me.
I kept waiting, thinking the “right moment” would eventually tap me on the shoulder.

→ A little more clarity.
→ A little more confidence.
→ A little less fear.

But that moment never arrived.

𝗙𝗲𝗮𝗿, 𝗶𝘁 𝘁𝘂𝗿𝗻𝗲𝗱 𝗼𝘂𝘁, 𝘄𝗮𝘀𝗻’𝘁 𝗮 𝘀𝘁𝗼𝗽 𝘀𝗶𝗴𝗻.
It was the doorway I pretended not to see.

I avoided decisions because I wasn’t sure which was right.
Not because I feared failing but because I feared choosing wrong.
And yet, every delay made the choice harder.

A few uncomfortable situations taught me something simple.

→ Clarity comes after the step, not before it.
→ Moving early builds more confidence than waiting.
→ The battle you choose matters more than choosing perfectly.

I’ve seen the same story unfold around me.
People stay stuck for years until life pushes them out of their comfort zone.

→ A new role.
→ A new team.
→ A change they didn’t plan for.

And suddenly they grew faster than they ever believed they could.

𝗠𝗼𝘀𝘁 𝗴𝗿𝗼𝘄𝘁𝗵 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗮 𝘀𝗺𝗮𝗹𝗹 𝗿𝗶𝘀𝗸.
Just one step outside what feels safe.

→ Try before you feel fully ready.
→ Give yourself room to learn while doing.
→ Accept that life doesn’t wait for perfect timing.

Either it’ll work out or it won’t.
But staying still ensures nothing ever will.

What’s the step you’ve been postponing that might finally move you forward?

#Growth #Mindset #Courage #Leadership #Change #PersonalDevelopment #FearToAction

𝗟𝗲𝘀𝘀𝗼𝗻 𝗳𝗿𝗼𝗺 𝗚𝗮𝗹𝗴𝗼𝘁𝗶𝗮𝘀 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗜𝗻𝗰𝗶𝗱𝗲𝗻𝘁 𝗮𝘁 𝗔𝗜 𝗦𝘂𝗺𝗺𝗶𝘁 𝗜𝗻𝗱𝗶𝗮.

It exposed the reality of today’s AI economy.

We don’t have thousands of AI companies.

We have

A few model builders.
And thousands of wrappers.

Most 𝗔𝗜 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 today is

→ UI on top of LLMs.
→ Workflow automation via APIs.
→ Integrations packaged as platforms.

That’s fine.

But let’s call it what it is.

𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 ≠ 𝗜𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻
𝗣𝗮𝗰𝗸𝗮𝗴𝗶𝗻𝗴 ≠ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
𝗔𝗣𝗜 𝘂𝘀𝗮𝗴𝗲 ≠ 𝗖𝗼𝗿𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆

As AI adoption grows, leaders must ask

→ Who owns the model.
→ Who owns the IP.
→ Who controls the dependency.
→ Who's just repackaging.

Without owning the foundation, you're just subscribing to someone else's vision.

#AI #Innovation #TechEcosystem #AIIndia #Leadership

𝗙𝗮𝗶𝗹𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.
Denying it is.

Someone once asked me,
“Tell me about your failures.”

“I never fail”, I said.
He was surprised.
So was I, why was that answer so surprising?

Ask anyone about failure and you’ll often hear deflection disguised as confidence.

→ It wasn’t my fault.
→ The timing was wrong.
→ The team didn’t deliver.

It always takes a few stumbles to understand.
Failure isn’t the opposite of success, it’s part of it.

When things go wrong, it’s natural to defend.
But with time and a little honesty, you realize

→ Most of us don’t lack capability.
→ We lack ownership, especially when things fall apart.

We switch jobs, hoping a fresh start will erase our mistakes.
but we leave behind the lessons we needed most.

If you were running a business, would you quit after one failure?
Or would you learn, rebuild and fail better the next time?

The best teams aren’t those who never fail.
They’re the ones who fail together, fix together and grow stronger together.

Because failure isn’t final.
Denial is.

And real courage isn’t loud
It’s quietly choosing to show up again tomorrow.

#Leadership #Growth #Mindset #Resilience #TeamCulture #Learning

For years, engineering leverage came from speed.

Launch faster.
Pack in more features.
Out-deliver.

AI has changed that equation.

When everyone can build quickly, the value comes from deeper understanding, not just raw output.

𝗢𝗹𝗱 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸

→ Writing lots of code.
→ Mastering frameworks.
→ Delivering more tickets.

𝗡𝗲𝘄 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸

→ Designing the vision.
→ Knowing customer needs.
→ Fitting tech to business goals.
→ Building systems that last.

If competitors can replicate your feature set in weeks using AI, what protects the product?

Not the code.

→ Unique data.
→ Customer behaviour patterns.
→ Integration depth.
→ Distribution advantage.
→ Brand trust.

The best engineers aren’t just generating code.
They design systems now.

#AIShift #SoftwareEngineering #ProductThinking #TechCareers #TechLeadership

Using AI for coding feels less like automation and more like mentoring.

Think of a very capable trainee.

Fast learner.
Reads everything.
Produces code quickly.

But still needs guidance.

If we

→ Define architecture and constraints.
→ Generate implementation.
→ Review assumptions and edge cases.
→ Iterate and stabilize the base.

Productivity compounds.

Once the foundation is right, output accelerates dramatically.

But if we skip that process and just accept whatever AI generates, a different pattern appears.

→ Defects propagate faster.
→ Security vulnerabilities go unnoticed.
→ Systems become harder to maintain.

The risk is not AI.

The risk is assuming it cannot be wrong.

#AIShift #SoftwareEngineering #DeveloperTools #FutureOfWork

Early in my career, I believed the best engineers were the ones who wrote code the fastest.

Twelve years later, my perspective has changed.

The strongest engineers I’ve worked with do something different first.

They pause.

They ask questions.

They simplify the problem before writing a single line of code.

AI tools highlight this even more.

They can generate code instantly.

But speed was never the real bottleneck in building good systems.

Clarity was.

→ Understanding the real problem.
→ Choosing the right constraints.
→ Designing something that still makes sense many years later.

AI accelerates the writing.

Experience shapes the thinking.

And thinking is still the hard part.

#AI #SoftwareEngineering #EngineeringLeadership #DeveloperExperience #Programming

It was a simple task.

→ Draft an email.
→ Clean up some notes.
→ Save a few minutes.

It felt harmless. Helpful, even.

Then I realized something later.

I 𝗵𝗮𝗱𝗻’𝘁 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 much at all.
I had just 𝗮𝗽𝗽𝗿𝗼𝘃𝗲𝗱.

𝗔𝗜 𝗵𝗮𝗱 𝗱𝗼𝗻𝗲 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸.

Since then, I’ve heard the same thing from many teammates.

“I know AI matters. I just don’t know how to use it properly.”

That’s not a skill problem.
It’s a thinking shift.

Working with AI isn’t about prompts or tools.
It’s about deciding where our thinking still matters most.

→ AI is great at drafting.
→ We are still better at intent.
→ AI moves fast.
→ We decide what’s worth moving toward.

The people getting real value from AI aren’t trying to master everything.

And you can too.

→ Use one or two tools daily.
→ Automate a task you already understand.
→ Ask clearer questions before asking for output.
→ Treat AI like a junior teammate, not an authority.

Over time, that changes how you work.
And how visible your thinking becomes.

Because the real edge isn’t speed.
It’s combining machine output with our judgment.

So the question isn’t whether AI will change how we work.

It already has.

The actual question is whether 𝘄𝗲’𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝘄𝗵𝗶𝗹𝗲 𝗶𝘁 𝗱𝗼𝗲𝘀.

#AIAtWork #AIInPractice #FutureOfWork #AIAdoption

Two employees joined the same company on the same day.

Both were talented.
Both worked hard.
Both had the same opportunities.

But they worked very differently.

Every day, Rahul spent hours doing everything manually.

→ Research.
→ Drafts.
→ Reports.
→ Emails.

He believed real work meant doing everything himself.

Arjun, on the other hand, had a different approach.

He used AI to

→ Brainstorm ideas.
→ Summarize documents.
→ Draft emails.
→ Automate repetitive tasks.

Rahul worked 𝗹𝗼𝗻𝗴𝗲𝗿 𝗵𝗼𝘂𝗿𝘀.
Arjun worked 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗵𝗼𝘂𝗿𝘀.

Time passed.

While Rahul was still busy finishing tasks.

Arjun was

→ Presenting better ideas.
→ Leading larger projects.
→ Solving higher-value problems.

Why?

Because while Rahul was working horizontally,
Arjun was moving vertically.

AI didn’t replace Arjun’s work.
It removed the friction around it.

Rahul eventually asked him,
“Don’t you feel weird using AI for your work?”

Arjun smiled and replied,

“I’m not using AI to do my thinking.
I’m using it so I have 𝗺𝗼𝗿𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸.”

The future of work isn’t about who 𝘄𝗼𝗿𝗸𝘀 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁.
It’s about who 𝘄𝗼𝗿𝗸𝘀 𝘁𝗵𝗲 𝘀𝗺𝗮𝗿𝘁𝗲𝘀𝘁.

And sometimes, the smartest thing you can do
𝗶𝘀 𝗻𝗼𝘁 𝗱𝗼𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳.

#AI #FutureOfWork #Productivity #WorkSmarter

If you are a fan of vibe coding,
This one is for you.

Amazon had an outage.
Significant disruption to their website.

Described as a 𝗵𝗶𝗴𝗵 𝗯𝗹𝗮𝘀𝘁 𝗿𝗮𝗱𝗶𝘂𝘀 𝗶𝗻𝗰𝗶𝗱𝗲𝗻𝘁.
Reportedly traced back to 𝗚𝗲𝗻-𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗰𝗼𝗱𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝘀.

Let that sink in.

Now imagine you are running that site.

Every minute of disruption means

→ Revenue lost.
→ Customer trust shaken.
→ Brand confidence impacted.

AI is now part of the development workflow.
But most organizations have not redesigned their 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 around it.

Teams are facing two common paths.

Either

→ You use AI as a helper.
→ You own the code.
→ You review everything.
→ Slower output. 
→ But you know what's running in production.

Or

→ There's pressure to ship fast.
→ AI tools are everywhere.
→ Delivery is the metric.
→ And somewhere in that rush, quality stops being the goal.

Most teams are leaning toward the second model.

Amazon's fix

Require senior sign-off on all AI-generated PRs.

Fast is fine.
But fast at the cost of stability and security is just a delayed incident.

Either fix the process now.
Or 𝘄𝗮𝗶𝘁 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗼𝘂𝘁𝗮𝗴𝗲 𝘁𝗼 𝗳𝗼𝗿𝗰𝗲 𝘆𝗼𝘂 𝘁𝗼.

#AILeadership #TechStrategy #SoftwareEngineering #AIGovernance

Open article: Amazon holds engineering meeting following AI-related outages by ft.com

Amazon holds engineering meeting following AI-related outages

𝗦𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝘀 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗲𝗻 𝗻𝗼𝘁 𝘁𝗼 𝘀𝗽𝗲𝗮𝗸.

Client meeting.

The client suggests adding new features into the current sprint.
The Product Manager is nodding along.

It sounds reasonable.
It sounds collaborative.

The timeline is already tight.

The Lead Developer sees it immediately.
This will derail the current deliverables.

He could push back right there.
Highlight the risk.
Expose the misalignment.

Instead

𝘓𝘦𝘵’𝘴 𝘴𝘺𝘯𝘤 𝘰𝘯 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘦𝘴 𝘢𝘧𝘵𝘦𝘳.

Meeting ends.

One quick message

→ Timeline impact.
→ Trade-offs.
→ Revised scope proposal.

No public conflict.
Client stays confident.
Team stays aligned.

𝗡𝗼𝘁 𝗲𝘃𝗲𝗿𝘆 𝗱𝗶𝘀𝗮𝗴𝗿𝗲𝗲𝗺𝗲𝗻𝘁 𝗻𝗲𝗲𝗱𝘀 𝗮 𝗽𝘂𝗯𝗹𝗶𝗰 𝗱𝗲𝗯𝗮𝘁𝗲.

Sometimes leadership is protecting the team
by 𝗰𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘄𝗵𝗲𝗻 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝗽𝗲𝗮𝗸.

#Leadership #Execution #Communication #TeamDynamics

𝗠𝗼𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗼𝘃𝗲𝗿𝗿𝗮𝘁𝗲𝗱.

Motivation feels good.
But it is unreliable.

Most people wait to feel motivated before they act.
That works for a day. Sometimes a week. Then actual work shows up.

Deadlines. Fatigue. Ambiguity. Repetition.

And motivation quietly disappears.

The people who consistently perform do not rely on feelings.

They rely on systems.

→ Clear 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 of what good looks like.
→ 𝗛𝗮𝗯𝗶𝘁𝘀 that remove daily decision making.
→ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 that forces follow through.
→ 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 designed to reduce friction.

Motivation reacts to success.
Systems create success.

𝗜𝗻 𝘄𝗼𝗿𝗸, 𝘁𝗲𝗮𝗺𝘀, 𝗮𝗻𝗱 𝗰𝗮𝗿𝗲𝗲𝗿𝘀
This shows up everywhere.

→ High performers show up even on average days.
→ Strong teams execute without constant pep talks.
→ Leaders build processes, not speeches.

If motivation was enough, consistency would be easy.
But consistency is engineered.

𝗦𝘁𝗼𝗽 𝗮𝘀𝗸𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗼 𝗳𝗲𝗲𝗹 𝗺𝗼𝘁𝗶𝘃𝗮𝘁𝗲𝗱.

Start asking:

→ What’s the smallest step you can do every day?
→ What plan still works when you don’t feel like it?
→ What keeps you on track when the hype is gone?

Motivation is a spark.
Systems are the engine.

What system are you relying on when motivation is gone?

#Habits #Discipline #Productivity #Systems #Consistency

𝗜 𝘄𝗮𝘀 𝘃𝗶𝗯𝗲 𝗰𝗼𝗱𝗶𝗻𝗴.
𝗔𝗻𝗱 𝗶𝘁 𝗯𝗿𝗼𝗸𝗲 𝗺𝘆 𝗰𝗼𝗱𝗲.

So I was working on a complex problem.

Not the kind you solve in one pass.
Multiple flows. Edge cases. Hidden dependencies.

I turned to AI to speed things up.

Got a solution back.
Looked correct.
Merged it.

Then few cases started failing.

Went back to AI.
Asked for a fix.
Got one.

Still failing.
Different cases this time.

This went on for a while.

On the surface, every solution AI gave me looked right.
But none of them were actually solving the full problem.

So I started tracing the flow manually.

→ What assumptions is this code making?
→ What happens when inputs change?
→ Where does this logic fail?

Eventually found the gap.
The AI-generated solution was 𝗺𝗼𝘀𝘁𝗹𝘆 𝗶𝗻𝗰𝗼𝗿𝗿𝗲𝗰𝘁.

It solved the obvious path.
But it missed the system constraints.

The interesting part,
It still led me to the right solution.

Very different from what AI suggested.
But reached faster because of it.

𝗧𝘄𝗼 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 stayed with me.

→ Planning matters. The clearer your thinking, the better the solution you get.
→ Every solution AI gave me looked correct on the surface. 
If I wasn’t paying attention, it would have gone to production.

This is 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗿𝗶𝘀𝗸 𝗼𝗳 𝘃𝗶𝗯𝗲 𝗰𝗼𝗱𝗶𝗻𝗴.

Not that AI gets it wrong.
But that it gets it 𝗮𝗹𝗺𝗼𝘀𝘁 𝗿𝗶𝗴𝗵𝘁.

And if you can’t tell the difference,
and are just accepting outputs

Then at that point

𝗬𝗼𝘂 𝗵𝗮𝘃𝗲𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗹𝗼𝘀𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲.
𝗬𝗼𝘂’𝘃𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗯𝗲𝗲𝗻 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗱.

#AI #SoftwareEngineering #DeveloperExperience #AICoding #TechLeadership

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Mayank Awasthi's Linkedin Analytics

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Mayank Awasthi's Linkedin Analytics

Performance ScoreMayank Awasthi's LinkedIn Performance

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