A Farmer's Son, a Tokyo Office and an AI Career | Scaler Academy Review
Raju Shivaram grew up in a small farming village in Karnataka, studied Mechanical Engineering at BMS College of Engineering in Bengaluru, and built a career that took him from full-stack development to DevOps, MLOps and deep learning. He also worked as a full-stack engineer in Tokyo before returning to India and joining Instalimb, where his work moved further into machine learning and AI. In 2022, he joined Scaler to strengthen his foundations and move deeper into advanced engineering and ML. He later describes being promoted to Senior MLOps Engineer with a 70% to 80% salary hike.
The striking part of the story is not only where it ends.
It is how many different places it passes through.
Beat 1: The Village
Raju grew up in a small farming village in Karnataka.
His father worked in the fields. His mother managed their home. Raju writes that his parents did not have much, but they never made him feel limited. Their expectation was straightforward: study well and build a stable career.
"I grew up in a small farming village in Karnataka."
Engineering became the first major step away from that village.
Raju joined BMS College of Engineering in Bengaluru and chose Mechanical Engineering. But during college, he began looking beyond his core discipline. He developed an interest in robotics and programming and started learning Java, Data Structures and Algorithms and competitive programming independently.
There was no single moment when his career suddenly became an AI career.
There was a gradual shift.
Mechanical engineering gave way to programming. Programming opened the door to software. Software created opportunities in other countries and other technical domains.
The path kept changing.
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Raju's first serious move into software came through self-learning.
He describes spending late nights learning Java, DSA and competitive programming while preparing for opportunities outside his core engineering field.
That preparation eventually created an unexpected opportunity.
During campus recruitment, a Japanese company visited BMS College of Engineering. Raju applied, interviewed and was selected.
The offer took him to Tokyo as a full-stack engineer.
Tokyo
The Tokyo chapter was important because the role exposed him to a much broader part of the engineering stack.
His team had only three engineers. That meant he worked across Ruby on Rails, PostgreSQL, JavaScript, DevOps and deployments. He also learned Japanese, with the company sponsoring language training.
The Tokyo job was not the end of the story.
His mother became sick, and he returned to India. The COVID-19 pandemic added further uncertainty. He then worked briefly in Bhopal and took up freelance work before joining Instalimb, a Japan-based startup operating in India.
The career that looked like a straight line from Bengaluru to Tokyo became something else.
It became a series of pivots.
Beat 3: The Method
At Instalimb, Raju began moving towards ML Ops, computer vision and deep learning.
The shift made sense given his earlier interests. He already had experience building software and working with deployments. Now he was becoming interested in what happened when machine learning systems had to work in real products.
By 2022, he felt he needed more structured support to go deeper into ML and advanced engineering. He joined Scaler in May 2022.
This part of his journey was less cinematic than Tokyo.
It was more methodical.
Raju's account describes a curriculum that began with foundations such as DSA, problem solving and SQL before moving into Python, machine learning, deep learning and MLOps. He also highlights system design, practical assignments and additional ML-focused sessions.
"What I really liked was that nothing felt rushed. Every part had depth, examples, and practical assignments."
For someone who had already worked in software, the purpose was not to start from zero.
It was to connect the pieces.
Raju describes using mentor sessions to work on problem-solving, cleaner code, system design interviews and career planning. Peer learning also became part of the process through mock interviews, assignments and debugging sessions.
The result was a broader technical profile.
Software development was still part of it.
So were DevOps, machine learning, computer vision and MLOps.
The farming village had not prepared him for those subjects directly.
But the habits needed to learn them were not so different.
Learn something.
Practise it.
Apply it.
Move to the next problem.
Beat 4: The Global Team
The Tokyo experience had already shown Raju what it was like to work in an international engineering environment.
His later work took that experience in a different direction.
At Instalimb, he moved deeper into ML Ops, DevOps and computer vision. His current work includes software and AI systems connected to prosthetics and orthotics. He describes working with computer vision models, CAD software, WebSockets, VPCs, and ML pipelines.
His LinkedIn account describes the transition clearly:
"Before Scaler – Full-Stack Engineer | After Scaler – Senior DevOps and MLOps Engineer."
The change was not from non-technical work to AI overnight.
It was a progression from one technical layer to another.
Full-stack engineering gave him software experience.
The Tokyo role broadened his exposure to DevOps and deployments.
Instalimb gave him experience with ML Ops and computer vision.
Scaler helped him structure the knowledge he needed to go deeper.
The result was a role where those pieces could exist together.
Raju says he was promoted to Senior MLOps Engineer and received a 70% to 80% salary hike. More importantly, he says the learning gave him greater confidence in explaining ideas, architecting systems and handling ML pipelines and deployments.
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There is an easy way to tell this story.
Village.
Engineering college.
Tokyo.
AI.
It would make for a clean narrative.
Raju's actual journey is less neat, and therefore more useful.
Mechanical Engineering did not lead directly to AI.
The first software role did not settle his career.
Tokyo did not become the final destination.
The pandemic interrupted the path.
Freelancing came next.
Then a startup.
Then ML Ops.
Then deeper learning.
That is what makes the story worth looking at closely.
A career does not always move in a straight line. Sometimes it moves by accumulation. Each role adds another skill. Each new environment changes what the engineer can see.
Raju's parents originally wanted what most parents want: for their son to study and build a settled career. The path took him much farther from the village than they could have mapped out in advance.
But the village remains the beginning of the story.
That matters.
Because the distance between a farming village in Karnataka and an AI engineering role was not crossed in one leap.
It was crossed one skill at a time.
"I have had, you can say, quite an eventful journey to establish my career."
Today, Raju works on systems that combine software, infrastructure and AI.
The starting point was still the same village.
The difference is what he built from there.
FAQs
Turn Learning into Career Growth
Can someone from a village background build an AI career?
Yes. Raju Shivaram's published account describes his journey from a small farming village in Karnataka through Mechanical Engineering, software development, a full-stack engineering role in Tokyo, DevOps and MLOps work, and deeper specialization in machine learning and computer vision.
His story is an individual example, not a guarantee. It shows that a village background does not prevent someone from building a career in AI or advanced software engineering.
Did Raju work in Tokyo?
Yes. Raju's account says he was selected by a Japanese company during campus recruitment and worked as a full-stack engineer in Tokyo. His three-person team gave him exposure to Ruby on Rails, PostgreSQL, JavaScript, DevOps and deployments.
What did Raju study with Scaler?
According to his published testimonial, his learning covered DSA, problem solving, SQL, Python, machine learning, deep learning, MLOps and system design, along with practical projects and mentor support. His LinkedIn post says he joined Scaler in May 2022.
What does Raju work on today?
Raju says he works at Instalimb on ML Ops, DevOps and computer vision related systems. His work includes ML pipelines and technologies connected to prosthetics and orthotics, including CAD and AI-based automation.
Distance Is a Solvable Problem
Raju's story starts in a farming village and passes through Bengaluru, Tokyo, Bhopal and back into a global technology environment.
The places matter.
So do the skills he collected along the way. But the most useful part of the story is the sequence. Mechanical Engineering led to programming. Programming led to software. Software led to DevOps. DevOps opened the door to ML Ops and AI.
There was no single leap.
There was a chain of them.
Scaler's wider learner base reflects that geographical spread too. The career report says learners in the reported cohort came from 3,123 pin codes across India. The number is large, but Raju's story gives it a more personal meaning. Geography can shape where a career starts. It does not have to decide how far the career can travel. Raju's story began in a village. But that doesn't define him. Don't let your surroundings limit you Explore Scaler's programs to know more.
