Start-up vs SME – Recognise the Difference and Get Hired Faster

So, you are looking for a job at a start-up, are you? Well, then it will do you good to understand the difference between a Start-up and just another small business, and also appreciate the nuances behind the recruitment strategies of a Start-up. Get a few tips too at the end of it all.

Why is a startup not just another SME (small medium enterprise)?


The typical SME is a business concern started primarily for earning a profit. It typically runs on its own steam and is focused on business at hand, how to increase revenues and turnover, how to cut leakages and increase profits, and how to increase efficiency. Strategies are based on market requirements and number of customers.

A start-up, on the other hand, has a different mindset altogether. It’s trying to create a new world with an idea or a succession of ideas. The focus is on innovation, creating value out of the idea, value for the promoters, for the employees, for the investment partners and shareholders, for its collaborators and partners, and for its customers.

And it is not any incremental value jump that a start-up is aiming at. The start-up strongly believes that the world will gain exponentially when the idea is fully baked. And the founder is fully aware that the baking will require tons of dedication and a committed way of life devoted to the idea.

The act of sourcing talent for a Start-up

Start-ups differ most from SMEs in the personnel front. Since it generally embodies a new business idea it requires multiple hands and minds working to develop it. The usual start-up is one where a number of heads do their thing simultaneously. That is also one reason why Building the Team is the most important task at the beginning of the Start-up’s life cycle.

Building a talent pipeline is critical for the company during these early days. And, to this end, creating a compelling employer brand and projecting a modern corporate culture help to yield top candidates. A start-up HR has the opportunity to craft the company story and build processes from scratch. Start-ups are also known to leverage unique sourcing strategies, particularly if they have support of large private equity partners or venture capitalists.

The recruiter for a start-up is generally on the look-out for versatile people since, in a small office, all hands on deck is a daily event. Hence, someone with a varied skill-set and who can be a team player will be in great demand. In the start-up environment one certainly gets to know everyone else well and definitely needs to learn to work with everyone quickly.

Tips for the Start-up Job applicant

Tip 1 is that the Recruiter is actually on a sales mission. He is not just trying to match CVs with profiles, but also to project the company in the right manner. He is more likely to be looking for flashes of brilliant thinking, drive and a positive attitude, and for people who fit in with the company’s culture.

Tip 2 is to demonstrate that you possess adequate communication skills and ease of working with others in a team. If you have genuine stories of how you have solved problems in business or real life, nothing like it.

Tip 3 is to be ready to make a pitch. The start-up recruiter is bound to ask the candidate to make his own sales pitch to determine the extent of passion and ideas in him, which often also reveals if he is a right fit or not. Convey your passion, motivation and convictions precisely. It’s very likely that the pitch can make it swing either way.

Tip 4 is to be Aware and Visible. Awareness is about the company, its product or mission, its culture and the industry in general. Visibility is about yourself, your LinkedIn profile and your online presence. Your professional connections and endorsements of your skills by others will count very much.

Tip 5 is to remember to be respectfully curious. You are interviewing the company as much as they are interviewing you, but do not make assumptions. Ask questions with a genuine interest to know more.

And, finally, Don’t pretend to be someone you’re Not.

Careers in AI and ML – What the job seeker ought to know

It doesn’t require any great perception to know that the field of Artificial Intelligence and Machine Learning presents a very lucrative employment opportunity these days. Expectedly, many of today’s youth are striving to pursue a career in this field. As innovations continue and AI-ML strengthens itself as a technology that can change society more than any other, the jobs being created in the sector are bound to grow multi-fold.

Qualities and Education

The best news for AI job seekers is that AI programming capabilities can be self-taught. And many different types of skill sets can come in handy. However, formal qualifications and specific skill sets are still sought for very much by recruiters. Having said that, let us take a look at what the specific capabilities and educational fields are that will interest the recruiter.

Abilities to innovate, think out of the box and solve problems are the top qualities hirers look for. At Cogniphi we also give value to a candidate’s habit of being in touch with the latest innovations in the IT industry, and good communications and other soft skills. Soft skills are as important as math and computing skills because AI/ML engineers work as part of multi-functional teams, and leadership and teamwork skills are needed to work effectively with colleagues and customers.

Indians do not have to be instructed to pursue formal education, because that is part of the general culture. And, it always helps. For AI jobs, HR recruiters now mainly target a few preferred areas of study like Robotics, Engineering, Physics, Mathematics, Statistics, Computer/Cognitive/Neural Sciences, Coding and Programming. Digital Humanities is also in demand as they teach students to collaborate more. Certifications will of course jump start careers.

Career Paths

Experts in select fields will always be snapped up by employers. But, for the new entrant, remember that it is the Software developer job that is the most common route to establishing a career in AI/ML. There will be countless opportunities for the developer who specializes in specific AI programming like training cameras or drones to understand what they see, or teaching smart online assistants to interact more intelligently, or improving the output from manufacturing robots.

The other obvious jobs like Data analyst, Software designers, Architects, Testing engineer, Algorithm specialist, Business analyst, Hardware specialist and so on will of course continue to grow in demand. Getting a head start and carving your path early will be a good advice to take.


The supply-demand gap for trained AI/ML professionals will only grow, for sure. The trend of Machine Learning engineers earning much more than the average software engineer will also continue.

Algorithmic coding and ML skills carry a premium, which is why salaries in AI and ML jobs are higher compared to other profiles. Of course, just possessing those skills don’t mean that you are the doyen of the AI world and you are eligible for fancy salaries.

Final pay scales always depend,without doubt, on experience, education background, interview performance and most of all, Attitude. This last parameter carries more weight than you may imagine.

Which Industries?

Although recent innovations like Alexa and driver less cars are what have brought AI/ML to the limelight, it has been a great career choice for some time now, mainly because the technology has been adopted across most industries and businesses.

The need for trained professionals is ever growing and there are several viable and unique jobs for the taking. From transportation to retail to health to manufacturing to security to entertainment to finance and a host of other sectors, the demand is near universal now.

Get Started

If you have strong computer and programming skills but are new to the AI field that you want to be a part of, take mathematics and ML courses, and get as much hands on training as you can. Get some exposure to general business knowledge also.

If you are already a programmer, you can focus on getting an understanding of algorithms and straight away go into coding.

If you are a data scientist wanting to take the leap into ML, first determine what you want to do. If you feel you are good at preparing data and also have sound business knowledge and communication skills, the best way forward is to acquire hard-core programming skills and get proficient at model building and visualization. There will be several areas of work in which you can decide to specialize.

Two things to remember in general:  One, there is no better learning than hands-on, and two, AI education can never stop at any stage of your career.

An engineering degree isn’t a must for a career in AI


Elon Musk Tweeted last year that his company is looking for AI developers and said that it does not really matter if applicants have any real degree, all they have to do is pass a “hardcore coding test”. While the SpaceX and Tesla CEO’s Tweet might have been bizarre for some people, it wasn’t entirely surprising. Over the past few years, there has been a growing consensus in the AI tech community that college degrees, especially engineering degrees, might be increasingly irrelevant for those looking to apply for jobs in the Artificial Intelligence space. Why is this happening and how is it possible to enter the world of AI without an engineering background?

A career in AI is mainly characterised by the use of sophisticated computer software and programs, automation, and robotics. And while all these three areas of study do converge in a conventional engineering degree, it is equally possible to acquire these skills outside a university. Those working AI opine that if an individual has a strong hold of math and statistics that is coupled with coding skills and knowledge of programming languages such as Python and R,and a good understanding of AI and it’s ability, then there is nothing stopping them from working in the field. Google and Apple have acknowledged that formal college degrees are no longer a prerequisite for applicants. This is mainly because companies that drive innovation understand that the value of people who are passionate self-starters and are willing to learn on the job.

Having said this, there will always be those who are worried about not having the backing of a formal engineering degree in order to get noticed by potential employers and recruiters. While this may be a valid concern, there are many ways to work around this problem of lack of experience or a formal degree. Personal projects that showcase basic machine learning skills and meet proper coding standardsare one way of doing this. More often than not, recruiters are always on the lookout for candidates who are self-starters and a personal project involving problem-solving is a great way of showcasing a proactive attitude. In addition to this, participating in Hackathons, coding challenges, and working on open source projects are a few other ways in which candidates can strengthen their recruitment potential even without an engineering background.

Many of the leading recruiters in the AI space give a great deal more priority to skillset than the nature of degree a candidate has. The key for them is to identify those who have excellent problem-solving skills in ,a real passion for technology, teamwork and dedication to work towards long term goals. In short, the problem-solver and hard worker is the real engineer even if he does not have a degree. A distinctive mark of such companies is often their interview process that focusses on foundations, coding skills and potential of the candidate to contribute to their overall business objectives. Forward-looking companies handpick their team of passionate tech enthusiasts and put them under the guidance of team leaders working on quality projects in multiple business sectors. That inevitably enables informal learning and building of AI skills and consequently enrich their career portfolio.

Career Artificial Intelligence Machine Learning Computer Vision Cogniphi

AI is destined to affect every industry in the near future, making it one of the most sought-after areas to build a career in. According to a Forbes Magazine article that recently sourced data from LinkedIn about hiring in the field of AI: artificial intelligence specialty hiring has increased by 74 percent in the past four years and these figures are only set to increase. There is also appreciation that what is taught in a generic college programme may be just a fraction of the specialized knowledge, awareness and skill that an AI professional will gather on the job. Although an engineering degree is viewed by AI talent hunters as a foundation for good technical education, the real challenge for them is how to nurture the degree holder and turn him into an AI technologist. Hence, it really is the best juncture to step into this field, and not having a formal engineering background is not an impediment to having a thriving career in AI.




Roadmap for Vision Intelligence and why more industries will embrace Vision AI

Over the past ten years, Artificial Intelligence or AI technology has hurtled towards unimaginable advancements. And even though most people remain unaware of what AI tech such as Computer Vision (CV) or Vision Intelligence entail, chances are that they’ve already used it. AI has ubiquitously entered all our lives; it is helping us to drive smarter, unlock our phones faster, shop better, and soon, it will be a part of almost every aspect of our lives.

What is CV or Vision Intelligence and why will we see more of it in the years to come?

As human beings, we have the amazing ability to sense our surroundings. With the help of our eyesight and cognitive capabilities we can visualize what is around us and make decisions based on what we see. Computers on the other hand, aren’t able to do this automatically. CV or Vision Intelligence is thus a subset of AI that enables computers to see, identify, and interpret visual data as humans would. The process is complex and requires vision algorithms and applications for the computer to learn. However, once the process is complete, computers can see, interpret, and analyse visual data much better and faster than any human ever could. In addition to being more efficient, Vision Intelligence is also an extremely malleable technology. From automobiles to agriculture, Vision AI can be tailored to meet the requirements of all sorts of industries and its uses are wide-ranging. Below are examples of how Vision AI is being customised to help a whole host of industries.


Vision Intelligence has been a revolutionizing force in the manufacturing space. From smart factory floors to quality control and accident prevention, Vision AI can help with almost every manufacturing process. In a modern factory setup, automated production lines are fitted with multiple moving machines such as conveyor belts and robotics units. For seamless production to continue, none of these systems can afford a breakdown. However, more often than not, stoppages do happen and they hamper production. Here is where Vision AI steps in. Armed with AI-based vision, CCTV cameras can analyse and diagnose every minor defect in a production line and issue real-time updates in case of machine failure or other problems. For example, if a conveyor belt is stuck due to improper material alignment, CV will preemptively flag the issue and notify the shift manager. Or, if a worker is standing too close to a vat of dangerous chemicals, AI-based CV systems can issue a red alert, thereby avoiding an accident. These are just a few examples of how AI has been a game-changer in manufacturing.


Retail is another sphere in which Vision Intelligence is creating ripples. For too long now, retail stores and supermarkets have faced a host of issues such as inventory mismanagement, revenue loss, and theft. Vision Intelligence has a solution for all of this. With the help of CCTV systems, and cameras placed on shelves and other crucial points, images of products and customers are captured, processed, and analysed to help retailers draw actionable insights. Vision-based tech and Deep Learning algorithms thus help generate insights like the effect of product placement on sales and customer shopping patterns in order to create more effective and personalised shopping experiences. AI Vision-powered cameras can also help to detect theft or incidents of sweethearting which is a form of theft where cashiers or checkout counter employees can give away merchandise to a “sweetheart” customer such as a family member or friend.


In healthcare, Vision AI has the potential to save lives. Technologies like Automated Pathogen Detection combine the power of AI and automation to help test samples of human tissue, sputum etc. in a faster and more accurate manner. Meanwhile, there are several other AI-based tools that are being developed to analyse three-dimensional radiological images – a process that could potentially speed up diagnoses and suggest much-more effective treatments for patients.

The above three industries are just a few of the examples out of a vast pool of sectors that Vision Intelligence is making a splash in. In the years to come, Vision Intelligence or Computer Vision will grow in its reach and capabilities, and more and more industries will realise its multifaceted potential.