Artificial intelligence and machine learning are the hot teas of the decade and will continue to remain so in the years to come. With progress in multiple fields of machine learning, some amazing applications come to life in the form of beautiful AI-based drawings or chat-based applications. All these advancements do pose a pertinent question of whether AI will be a threat to traditional jobs. We shall thus highlight some technology jobs that AI will help redefine and optimise, but not replace.
Software Developer
A complete software product of any kind goes through a journey, where we first gather all the requirements for the several features that will be part of the software, the different metrics on which the success or failure of the features will be based, and also take decisions around the architecture of the system, keeping in mind the most optimum tools to use.
For instance, the choice of databases that will power the system or the thought process that goes into converting strictly business tasks to code, all are decisions that require intuitive, empathetic, and strategic decision-making in the process. There is also the added angle of managing collaboration across different teams that contribute to a single system.
People Turn Ideas Into Reality
It takes a team of developers to turn business ideas into reality. AI can aid a developer in the speed of their day-to-day work so that they can focus on better optimisations in the system, delegate some mundane tasks, and believe they will be solved optimally. One can also save time in reviewing code or queries if we get to a stage where AI tools can produce these details accurately.
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QA Engineer
The role of a QA engineer demands checking a product for various complex business scenarios. They have to dawn the hat of multiple users and think about the usability of the product from all those perspectives. A QA engineer needs to interact with the software developer, product, and design to understand the complete rationale behind a feature and also intuitively differentiate between a bug and a feature.
Human Intervention Is Essential
Again, AI can automate several parts of their journey and eliminate redundant or repetitive tasks. For instance, AI can help automate integration tests that a QA uses to check if an entire integrated system, with different parts, is working perfectly. One can also use tools like rule engine to simulate expected behaviour under different business use cases. But it would still take human intervention to decide on all the test scenarios for a given product and then use the tools to automate the process of testing, and also evaluate if the actual outcomes from a product are indeed the expected outcomes.
DevOps
A huge aspect of software development is the part where others can use the software that has been developed. To use the software, one needs to deploy the code and the associated underlying infrastructure. In the simplest terms, if one wants to use google.com, it needs to be available on the defined web address and be accessible via the address. Deployment of the software and associated infrastructure, like database, system monitoring tools etc drive the usability of a product by making it quicker to deploy in case of any changes. It also protects the product from failures by controlled rollout or strategising roll-outs according to different time zones or high-demand areas.
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Example: Content Release On OTT
For instance, an OTT platform like Netflix shows different content in different regions. So if new content is to be released, one can control the release according to the time zones. If the release time happens to be midnight Asia time, then other regions can monitor if the release in Asia went well, and then subsequently release the content in their areas, well before folks living in America or Europe start their day. All of these require customisations and tribal knowledge of the system, and the ability to decide on a tool/product for infrastructure when multiple tools offer similar advantages.
AI Can Perform Level-1 Tasks
AI can again work as a tool and optimise devops and help folks focus on better problems, by eliminating extremely tedious tasks, like searching for an issue in a thousand lines of logs - typical problems that involve searching for a needle in the haystack or problems which have specific patterns that can be automated to save more time. The role of devops engineers can evolve into using AI in a better manner in their day-to-day job to eliminate redundancy in initial setup or all the level-1 tasks that are performed by humans at the moment, but it will not render their roles obsolete.
Some technological jobs will be redefined and optimised by AI, but they won't be replaced.
Security Engineers
The nature of cyber threats is becoming increasingly more creative. With the landscape changing so rapidly, it needs human experience to deal with certain problems faster than an algorithm. Security changes not only deal with technical issues but also involve business risk and compliance with certain regulations.
For instance, according to some RBI regulations, companies can not save customer-sensitive data, so security engineers would need to ensure that sensitive, private information is not exchanged or leaked in any way from any part of the system.
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Why AI Won’t Suffice
AI algorithms might also require massive data points to learn, which might not always be available. Sometimes AI would need to know which problem is more important than the others and in a time-critical job, the lack of such intuition might be counterproductive. The algorithm might give more importance to one problem than the other, thus raising false positives and negatives, which are detrimental in security scenarios specifically. AI can be involved in acknowledging an attack sooner and proceed with the basic investigation of determining the IP address, affected nodes, etc. Thus, AI will again help eliminate redundant parts of the process.
Data Science/ML Engineers
We would need data scientists/ machine learning (ML) engineers to come up with new algorithms to optimise the existing ones and find solutions to new challenges. Multiple fields can benefit from the help of machine learning algorithms, like supply chain management or quality control in large-scale factories. A convoluted neural network might also need to be debugged for some part to optimise or correct the expected behaviour in case some changes are expected and this requires human intervention to understand and unravel the code, in order to find the fixes.
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Performance metrics monitoring of the algorithms that are being used and taking subsequent calls on improvement or trying a new approach can be taken well by an intuitive human. Again, an ML engineer also needs to interact with multiple stakeholders to understand their problems and devise an approach that best suits their needs. For instance, an algorithm to help a student with special needs would require one to understand with empathy all the problems they face and work up a solution that models their needs the best.
In a nutshell, AI will aid and assist several job roles in the future, while optimising all the redundant tasks and redefining the scope of the roles to a huge degree, thus opening up the world to more advancements in technology.
Deboshree holds a BTech in Computer Science and Engineering from BIT Mesra. Backed with 6 years of experience working with Goldman Sachs and Walmart, she currently works with Cred as backend engineer.