Hi there!
I’m Rasan Samarasinghe, the creator of itpro.lk. I warmly welcome you to ITPro Insights, the official blog of ITPro.lk.
Here, I’ll be sharing valuable content for both job seekers and recruiters. Whether you’re looking for your next career opportunity or seeking top tech talent, this blog will have something for you.
I’d love to hear your ideas and suggestions for topics you’d like us to cover. Thank you for joining us!
Warm regards,
Rasan
Job searching is already stressful. When candidates spend hours tailoring CVs, writing cover letters, and preparing themselves only to hear nothing back, it can be frustrating. One major reason behind this frustration is something called ghost job postings.
This is a growing global issue, and Sri Lanka is not immune.
A ghost job post is a job advertisement published without a real intention to hire at that time.
The job may look real. The company name may be familiar. The role description may sound exciting. But behind the scenes, there is no active recruitment happening.
The post exists for other reasons, and not to actually fill a vacancy.
Ghost job postings cause damage on multiple levels.
For job seekers, they:
For companies, they:
At a broader level, ghost jobs:
Studies in the U.S. estimate that around 27% to over 30% of job postings are ghost jobs. Many recruiters and companies have openly admitted to posting ghost jobs at least once.
In Sri Lanka, there is no reliable research or published data on this topic yet.
However, considering:
…it’s reasonable to assume that ghost job postings may exist at similar or even higher levels, especially in tech.
Companies post ghost jobs for many reasons. Some are intentional, some careless, and some unethical.
Common reasons:
Not all reasons are malicious, but still making problems.
Not every suspicious job post is fake. But ghost jobs often follow patterns. If you notice several of these together, pause and research before applying.
Common warning signs:
If something feels off, trust your instinct and do a quick check.
Practical steps:
If applying via email:
Some rules should never be broken. No matter how desperate the situation feels.
If you come across a suspicious or fake job ad on ITPro.lk, please use the Report option on the job page.
This helps us protect the community and keep the platform trustworthy for everyone.
If you’re a company posting ghost jobs and believe it’s harmless. It’s not.
There are better, honest alternatives.
If you want to show openness to talent:
If you want to collect CVs:
If you want engagement:
You don’t lose credibility by not hiring all the time.
Trust is the heart of any strong brand.
Companies don’t build trust by posting frequent job ads.
They build trust by being honest, transparent, and respectful of people’s time.
And for job seekers research is your superpower.
A healthier job market starts with honesty on both sides.
AI has moved beyond experimentation and into real business impact. Every day, we hear news about rapid advancements by major AI companies, traditional business systems being transformed with AI, and workforce reductions driven by automation.
Amid all this noise, it has become increasingly difficult to clearly understand how AI is truly impacting our industries, especially IT service outsourcing.
Like any major technological shift, the core goal of AI is automation. This naturally changes the demand for manual labor and reshapes how work is done. Sri Lanka’s IT industry, which is heavily driven by service-based outsourcing, is particularly affected by this disruption.
Routine tasks such as helpdesk support, basic coding, software testing, and data entry are increasingly automated by AI-powered tools. This reduces the need for large teams handling repetitive tasks, directly threatening traditional outsourcing models.
Outsourcing has traditionally relied on lower labor costs in offshore locations. As AI replaces human effort, the cost advantage of offshore services becomes less significant.
Small and mid-sized companies in high-cost countries can now use AI to perform tasks they previously outsourced. This shift may shrink the overall outsourcing market.
Clients may expect higher efficiency and more value at lower prices, as AI promises cost savings. This creates additional pressure on outsourcing vendors to deliver more for less.
Service providers can integrate AI into their offerings such as intelligent monitoring, predictive maintenance, AI-powered customer support, and AI-assisted coding.
These enhancements make services more efficient, scalable, and valuable.
There is growing demand for AI strategy, integration, training, and ongoing support. Companies with AI expertise can offer new services such as model deployment, data engineering, AI governance, and compliance solutions.
Organizations can operate with smaller, highly skilled teams that use AI as a productivity multiplier. This improves margins and enables companies to move up the value chain from “labor providers” to “strategic partners.”
Skills such as creativity, critical thinking, problem-solving, client relationships, and high-level system design are difficult to automate. Outsourcing companies can reposition their workforce toward these areas while allowing AI to handle repetitive tasks. This shift emphasizes experienced professionals over low-cost labor.
Predicting the exact impact of AI is difficult, but it is clear that both risks and opportunities lie ahead for the outsourcing industry.
Shrinking the workforce size is a clear sign and already happening. How our industry embrace this change?
Companies must rethink their workforce strategies favoring smaller, expert, veteran teams over large workforces focused on mundane tasks. For those willing to adapt, AI presents a massive opportunity to deliver smarter, faster, and higher-value services, ultimately moving them up on the value chain.
“Talent retention” is one of the most frequently used phrases in the IT industry.
Company presentations highlight about it, HR policies built around it, leadership always talk about it in townhall meetings.
Yet, in reality true talent retention in many IT outsourcing companies feels more like a myth than an achievable outcome.
At its core, IT outsourcing is project-driven. Engineers are hired to work on client requirements, timelines, and budgets that are outside the company’s control. When projects end, change direction, or stop funding, teams are reshuffled or released.
This creates an environment where stability is promised, but uncertainty is built into the business model.
Unlike product companies, outsourcing firms rarely build something they fully own. Developers often short-term deliverables. Over time, this limits emotional ownership and long-term attachment to the work.
Outsourcing companies often argue that exposure to multiple clients accelerates learning.
But is that a truth? Developers may gain surface-level exposure to many technologies but not always the enough depth to master.
Career growth is constrained by:
Flat role structures
Promotion cycles tied to headcount needs, not merit
Titles that change faster than responsibilities
In countries like Sri Lanka, the IT talent market is highly competitive. Skilled professionals are approached with:
In such an environment, loyalty alone cannot compete with opportunity.
Keeping large numbers of engineers for the long term is seems impossible, especially in IT outsourcing. We should accept it as it is rather trying to protect what isn’t actually works.
Instead of asking, “How do we keep people for 5–10 years?” a better question might be
“How do we create a place where people do their best work while they are here?”
Shorter tenures are not necessarily failures. If an engineer leaves after a short period of time with stronger skills, positive experiences, and professional respect, that relationship has still delivered value on both sides.
Companies that focus on transparency, meaningful work, and realistic expectations may not retain everyone but will earn a stronger reputation, better referrals, and quality talent over time.
There is a growing misunderstanding about what a university degree is meant to do. Specially in the IT industry. Many people expect degrees to work like vocational training programs. This expectation creates frustration for students, employers, and educators alike.
A degree is academic by nature. Its primary goal is to provide academic value, not to train someone for a single, specific job role. Degrees focus on building strong foundations: critical thinking, problem-solving, theory, and long-term understanding of a field.
This does not mean degrees are useless for careers.
Some degrees are designed to produce professionals for clearly defined roles. A good example is MBBS, which leads to a specific profession. However, this is not how most degrees work. Especially in fields like IT, engineering, or management.
At the same time, it is also incorrect to say that degrees only deliver theory with no relevance to jobs. Degrees aim to prepare students for a range of job roles within an industry, not one guaranteed position. They provide a broad academic base that allows graduates to adapt, learn, and grow over time.
Vocational training programs are different by design.
They focus on training people for specific job roles. These roles usually exist in industries with stable and predictable demand. Examples include plumbers, welders, carpenters, electricians, and mechanics.
Vocational courses are practical, role-focused, and short-term. Their success depends on how stable and clearly defined the job market is.
The IT industry does not work like traditional vocational industries.
1. Technology Changes
Technology changes fast. Job roles evolve quickly. Tools, programming languages, and frameworks rise and fall within a few years. Because of this, it is very difficult and even impossible to design a long-term vocational training that guarantees relevance.
What is in demand today may be outdated tomorrow.
2. The Nature of the IT Industry in Sri Lanka
Sri Lanka has a large and diverse IT services sector (majority doing outsourced services). Many companies do not build the same type of products or stick to the projects that use same technologies.
Is it a client who wants to build new products using the latest technologies? Is it a need on maintaining and extending legacy systems built using older tools and platforms?
Because of this mix, even companies struggle to clearly define the exact workforce they need. A “job-ready” graduate for one company may be completely unsuitable for another.
The confusion affects both sides of the industry.
Many IT companies expect IT degrees to train graduates exactly for their internal job roles.
Many students expect degrees to train them for a specific role and guarantee employment.
Both expectations are unrealistic.
Degrees can only prepare students with strong academic foundations that allow them to adapt to different technologies, industries, and career paths.
If we want a healthier IT ecosystem, we need to change how we think.
Degrees should be respected for what they are: academic programs that prepare people to think, not just to follow tools.
Job readiness comes from opportunity, training and continuous learning, not from degrees alone.
Understanding this difference benefits everyone including students, employers, and the industry as a whole.
What You Must Know About the People Giving It
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Almost every IT community forum have become extremely noisy places.
Many people are asking questions about choosing a career path, selecting a degree, or figuring out what’s “safe” for the future. Some replies are very positive, others are extremely negative, and most people are simply unsure about which direction to take.
This is understandable. The tech world is changing fast, and uncertainty makes people talk more. But one thing we often ignore is that career advice is never neutral. Whether people realize it or not, their opinions come from their own motivations, fears, insecurities, or past experiences. That’s why it’s important to look not just at what someone says, but why they might be saying it.
People who work in public universities, research institutes, or schools usually come from a very structured academic background. Their world revolves around theory, specialization, and traditional study paths. They’re usually not very connected to the business side of IT or the realities of the private sector.
So the advice they give often reflects what they know best: long academic routes, deep specialization, and stable, predictable career paths. This can be useful, but it doesn’t always match the speed and unpredictability of the tech industry.
Then there are the private universities and training centers. Their environment is different. Education is also a business for them. So even when they genuinely want to guide you, there is always a commercial angle behind their recommendations. If you tell them you’re interested in something, chances are they have a matching course to offer. Some will even shape your thinking in a way that leads you toward their programs. It’s important to keep this in mind and evaluate their advice with a bit of caution.
This group is often overlooked, but they are very influential. Especially in online discussions.
These are people who have already chosen their path and invested time, money, and effort into it. Naturally, they don’t want to see their field threatened or questioned. When someone asks whether their chosen field will have demand in the future, they often respond with a lot of emotion. They defend their path because accepting uncertainty means accepting they might have made a wrong choice. This doesn’t mean their opinion is invalid, but it is definitely shaped by wishful thinking.
Industry professionals usually give very practical advice because they are living in the real world of tech jobs. They know which skills matter, what companies look for, and what the day-to-day work actually feels like.
However, this group also has its own dynamics. IT is competitive, and sometimes people don’t want more competition in their area. So depending on how they perceive you, the tone of their advice can change. Some will encourage you, while others may discourage you. Without even realizing they’re doing it.
Finally, there are the founders, entrepreneurs, and managers. These people understand the market better than most. They know what skills are in demand, what types of talent companies are searching for, and how the industry is moving.
Their advice can be very accurate, but even they have a certain viewpoint. They often see everything through the lens of business needs. If they think you would fit well into the structures and roles they value, they will guide you one way. If not, their advice may lean in another direction.
Seems every group comes with a bias, naturally. Everyone speaks from their own world. That’s why the key is not to blindly trust any single source. Instead, try to understand where the advice is coming from.
We don’t need to avoid advice. Just need to evaluate it.
Career choices become clearer when you stop absorbing noise and start observing intentions. The more aware you are of the motivations behind the advice, the easier it becomes to make decisions that truly fit you.
Assessing technical skills is never easy. A technical interview is useful not only because it tests coding ability, but also because it reveals how someone thinks, communicates, and approaches problems. That’s something you don’t always get from a written test or assignment.
Traditionally, interviews focused on algorithm puzzles, syntax, and textbook system design. But in today’s world, where tools like GitHub Copilot and ChatGPT can generate code instantly. What matters now is how engineers use these tools wisely, review what’s produced, and make sound decisions.
Here are some types of interview questions that matter more in the age of AI:
Q: If an AI assistant generated this code snippet, how would you check whether it’s safe, correct, and efficient?
A strong answer should include testing edge cases, checking for security issues, and making sure the code is clear and maintainable, and not just trusting the AI output blindly.
Q: Here’s a function that works but is failing in one edge case. Walk me through how you’d debug it and identify the issue.
This shows how the candidate reasons through a problem, not just whether they can fix it.
Q: Design a job recommendation system that uses an AI model. How do you handle cases where the AI gives irrelevant or biased suggestions?
Good answers mention fallback logic, monitoring, feedback loops, and awareness of fairness and bias.
Q: Imagine you need an AI to generate boilerplate code for a REST API. What would your first prompt look like, and how would you refine it if the output wasn’t good enough?
This tests whether the candidate can clearly frame problems and work with AI as a collaborator.
Q: An AI tool suggests a clever one-line solution, but it’s hard to read. Do you keep it or rewrite it? Why?
A thoughtful answer should focus on maintainability and clarity, knowing that humans will maintain the code.
These questions go beyond memorizing algorithms and simple coding drills. They’re about how candidates think, adapt, and make decisions in a world where AI can generate code instantly. They highlight skills AI can’t replace: reviewing and reverse engineering AI outputs, debugging edge cases, framing problems clearly, handling bias responsibly, and prioritizing readability over shortcuts.
Boilerplate may be automated, but judgment, problem deconstruction, and collaboration remain uniquely human, and that’s what modern interviews should uncover.
Originally posted on Medium, can read it here: Go to Medium article
In January this year, we introduced ITPro Ratings, a feature that automatically scores each job application submitted through our platform. Since then, the number of rated applications has grown steadily alongside the rising popularity of ITPro.lk. We’ve now crossed an exciting milestone of over 20,000 applications rated by our AI.
Recruiters can view a list of applicants and their CVs directly from their dashboard. Each application is given a rating out of 10, automatically generated by our AI. This score is calculated by comparing the content of the candidate’s CV with the job advertisement. In addition to this number, it gives recruiters an AI-powered insight into how well an application matches the advertised role.
ITPro is a high-visibility marketplace for tech job ads in Sri Lanka. A single job post can attract hundreds of applications. If you’re a busy recruiter receiving, say, 500 CVs for a single role, manually reviewing each one may not be realistic. ITPro Ratings allows you to sort applications by relevance, helping you focus first on the ones that are most aligned with your requirements.
That said, ITPro Ratings does not attempt to evaluate candidate competency or technical skills. Skill evaluation is a nuanced and context-specific challenge that cannot be fully solved with a one-size-fits-all solution.
This raises a valid question: Is it justifiable to shortlist candidates based purely on the language used in their CVs? Let us know your thoughts in the comments!
Recruitment is fundamentally a human process built on relationships and context. Automation can help, but only to a point. That’s why we’ve designed ITPro Ratings to support, not replace, human decisions.
We understand that job seekers worry their applications might be rejected without ever being seen by a human. On ITPro.lk, applications are always presented to recruiters in chronological order, and the system never discards or auto-filters any applications. The rating is simply an additional insight, not a decision-making tool.
Here is the chart showing the percentage distribution of AI rating scores. You can clearly see that most applications received a rating between 6 and 8, with 8 being the most common score. The internet already has plenty of CV guides, templates, and services, which has helped to raise this standard.
Here’s the chart showing the monthly trend of average AI ratings from January to July 2025. You can observe a general upward trend, with July 2025 having the highest average rating so far.
We measure feature adoption using standard analytics, page hits, dashboard usage, and feature interactions. Based on current data, usage is moderate. Recruiters are exploring the AI feature, but the core value of ITPro remains in giving your job ads wide visibility, not necessarily in AI-driven automation.
We’ve noticed a subtle trend: some candidates are now focusing more on polishing their CVs to align with AI evaluations, sometimes at the expense of actually improving their skills. (Related post: Polishing Your CV, is Really a Solution?)
We’re not making conclusions yet. We just observing. The full impact of AI in recruitment is still unfolding.
AI can support recruitment, but it shouldn’t define it. Even with all the algorithms, automation, and analytics, gut feeling and human intuition though imperfect, still play a powerful role in hiring decisions. It’s that blend of data and human judgment that creates meaningful outcomes.
Have you ever seen the following issues in the management of people at organizations?
Chaos in autonomy-led control in a new team with no clarity
Stagnation in Process-led control in a creative team
Micromanagement and silos in leader-led control in a 200-person organizations
The challenges described above arise when organizations misapply distinct control models. These models represent different approaches to managing people, and using the wrong model at the wrong time can create significant friction. This post explores these management models, their strengths, when they work best, and when they fail.
Follow the person, not just the process.
The leader-led model relies on relationship-based control, where work is driven by personal loyalty, status, or hierarchy rather than processes or outcomes. Traditional hierarchical companies or early-stage startups with charismatic founders often adopt this approach.
Pros: This model fosters high trust in small teams, enabling rapid decision-making due to strong leadership. It excels when loyalty and relationships outweigh formal systems, such as during a crisis when decisive command is critical.
Cons: Scaling is the primary challenge, as decisions often bottleneck at the leader. This can encourage favoritism, suppress dissent, and harm team morale if the leader is toxic or inconsistent. The model lacks sustainability if the leader steps away or burns out.
Trust through accountability and purpose.
In the autonomy-led model, employees are given goals but have the freedom to decide how to achieve them. Control is maintained through outcomes and peer accountability. This approach is common in high-trust environments, such as R&D teams, product startups, and senior-level roles.
Pros: This model excels when employees are skilled, motivated, and aligned with organizational goals. It fosters creativity, speed, and adaptability, making it ideal for company cultures that prioritize trust, transparency, and shared objectives.
Cons: The model fails when teams are inexperienced or lack clear direction. Without well-defined metrics or boundaries, it can descend into chaos.
The system is the boss.
Employees adhere to clearly defined workflows, rules, standard operating procedures (SOPs), and timelines. Tasks are predictable and often repetitive. This approach is widely used in industries such as manufacturing, business process outsourcing (BPO), logistics, and compliance-heavy environments like banking and healthcare.
Pros: This model excels when employees are less experienced or when organizations operate at a large scale. It ensures scalability and consistency, making it ideal for franchises, manufacturing, or environments where compliance and risk reduction are critical.
Cons: The model struggles in ambiguous or creative environments where innovation is needed. Employees may feel like cogs in a machine, leading to low morale due to limited ownership or rigid roles. It also fails to adapt to rapid changes or market shifts.
In reality, most organizations blend these three, but the dominant one shapes their culture and performance. The optimal mix depends on the nature of the work (routine versus creative), team maturity, and the company’s stage of growth.
Example 1:
Startups often begin with a leader-led approach, transition to autonomy-led as they scale, and formalize operations with a process-led model when executed effectively.
Example 2:
A big IT company might start with a process-heavy setup to keep everything running smoothly. Later, they give more freedom to small product teams to move faster and be creative. And when something important or urgent comes up, leaders step in to make quick calls and keep everyone on track.
Yes, and in most cases, we should. The best organizations use a mix of all three, depending on what the situation calls for.
It’s about knowing when to shift gears. The smartest teams flex between them to get the best results. Choosing the right model boosts engagement and productivity. Great teams don’t just happen. They’re built on the right kind of control.
Many freelancers or solopreneurs think about expanding their work into a business venture someday. Many, at least try, and some actually realize their dream.
Transitioning from freelancer to business owner involves numerous tasks and significant changes. This post aims to talk about hiring and approaches you can take.
Hiring interns or junior-level helpers at a low cost is often the first step many take when moving from solo work to team-based work. Especially in self-funded setups, this approach is common due to its lower financial burden. Interns can often be found with minimal effort and short lead times.
However, training them takes time. Without a clear plan for mentorship, juniors may struggle, underdeliver, or leave early. Their output may affect timelines or quality if not closely supervised. There’s a risk that, instead of saving time, the workload increases. Simply treating interns as cheap labor tends to backfire unless there’s readiness to invest in their development.
Choosing to hire someone more skilled or experienced is a different kind of move often a strategic one. This route appears more common when the goal is to improve creative quality, scale faster, or delegate leadership responsibilities.
Though this option comes with a higher cost and sometimes emotional challenges around hiring someone more capable. It often leads to stronger long-term outcomes. It may require higher pay or revenue sharing, and a shift in how leadership is viewed. In these cases, the founder’s role moves from doing everything to guiding, enabling, and removing obstacles for others.
Interestingly, high-talent individuals tend to attract more talent. When skilled people are brought in early, they often shape the team culture and help draw in others of similar quality.
A pattern observed in some of the most successful transitions is a hybrid path. Hiring one highly skilled person first, even at a higher cost, to help set up strong systems and processes. Once that foundation is in place, junior hires can be added and trained within a more stable structure.
In most cases, the first few hires define the direction, quality, and culture of the team. Whether the goal is scale, quality, or freedom, those early decisions tend to echo throughout the future of the business.
“Learn the fundamentals so you’ll succeed in your endeavor” This is a common piece of advice people often give. You’ll frequently hear it when asking how to secure your career or how to learn a new technology, etc.
In today’s world, overflowing with knowledge and information. This advice is vague and it leaves us with unanswered questions. What exactly should I learn as fundamentals? How can I be sure I’m learning the right things?
So when someone says “learn the fundamentals”, the right response is:
“Which fundamentals, and how deep, based on what I’m building?”
Necessary if you’re doing hardware design, building compilers, or working with embedded systems. Not necessary for typical software developers.
This area consists of concepts CPU cycles, memory hierarchy (RAM vs cache), processes, threads, virtual memory, file systems.
This is necessary if you’re into systems programming, performance tuning, or OS development.
This is about syntax, parsing, type systems, interpreters vs compilers.
This is necessary if you’re building tools (like linters, and transpilers), designing new languages, or deep into back-end design.
Concepts and implementation of arrays, linked lists, trees, hashmaps, sorting, recursion, time/space complexity.
This is essential for all developers. They underlie everything, from efficient code to debugging performance issues.
This is about the theory of HTTP, TCP/IP, DNS, client-server model.
This is necessary if you build web apps, mobile apps, APIs, etc. But you don’t need to know TCP flags or OSI model layers deeply unless you’re in DevOps or network engineering.
This is about the design of databases, SQL, indexing, normalization, and transactions.
Learning databases is essential for most types of app developers. You can’t avoid data persistence.
This is about learning Git, CI/CD, testing, and packaging tools.
It is required for modern software development in teams and production settings.
Software architecture becomes fundamental once you move from building apps that “just work” to systems that are “designed to last.” Architecture is essential at certain points in a developer’s or engineer’s journey. especially when working on large-scale, long-lived, or team-based systems.
The fundamentals required for AI vary depending on the role you’re aiming for.
whether you’re a machine learning engineer, data scientist, AI researcher, ML ops engineer, or even a developer using AI APIs these are the core knowledge areas required for that.
Final thoughts:
My list is limited to few common IT and Computer related fields. There are many more.
When learning fundamentals, you don’t need to “boil the ocean” of computer science. As a practical use, go just deep enough to understand what your tools abstract away. And just go deeper when things break, or performance matters.
