Understanding new-age AI-led career options

Understanding new-age AI-led career options

Recently an advertisement by a well-known brand was up on Linkedin, looking out for Principal Technical Program Manager in AI/ML/Video Understanding.

Recently an advertisement by a well-known brand was up on Linkedin, looking out for Principal Technical Program Manager in AI/ML/Video Understanding. The job description asked for a person who would not only have the technical skills but also possess the wherewithal to imagine the impact they would create for the future of the industry.

At a time when the video technology industry is disrupting a decades-old industry through cloud services, multi-dimensional computer vision with projectile visual effects, content-based video retrieval along with machine learning, many of the future generation would be interested in knowing what it takes to be a part of this. I say this not only because these job roles come with good remuneration packages but also because these profiles are challenging and rewarding with opportunities for accelerated career growth.

In recent times the video tech industry has been consolidated with new age equipment, superior technology and some astounding innovations that are not a mere hyperbole changing the scenario. It has made trailblazing progress that has redefined the entire industry and unlocked work efficiencies.

AI Research Scientist

An Artificial Intelligence (AI) research scientist plays an important role in conducting and analysing data to offer a viable solution. For easier understanding, an AI research scientist helps in creating multiple sets of algorithms of neural networks through a process called 'deep learning'. The remarkable difference between Machine Learning (ML) and Deep Learning (DL) is derived from human data. While the former organises and processes the data before incorporating it into an algorithm, the latter literally needs no human intervention to process a gigantic amount of raw data. Aspirants with a minimum of post graduate computer degrees, mathematics or computer analytics, good knowledge of computer languages especially Python.Theoretical and empirical research and for addressing research problems. Analytics helps a researcher translate and connect variables, inquisitiveness enables a researcher to presuppose and offer a solution and creativity encourages lateral thinking. It is extremely beneficial for an AI research scientist if their interest aligns with the corresponding industry for a better career advancement.

In May 2022, Statists stated: "The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes."

Data Scientist

Our own head of data science Edmond Chalom humours how to think like a data scientist while the knowledge of statistics, probability, neural networks and programming help, the aptitude for problem-solving helps abundantly. But being proficient in several programming languages aids, Python has gained great traction after its augmentation in thriving technologies. A data scientist typically defines the problem, analyses data, measures plausible outcomes and plans out experiments that would lead to definitive conclusions in lieu of an individual or an organisation. A certification in machine learning, adept information on retrieval techniques and methodologies, most importantly knowledge in algorithm building and decoding data structures. However, a technologist needs to evolve in order to innovate new technologies. 'Five of the world's biggest technology companies contribute to more than 50 per cent of the world's market capitalisation and are the biggest employers of data scientists and engineers. And technology is only one of many sectors that hires data scientists.'

Machine Learning Engineer

Despite the overlays in the job profile of a data scientist, data analyst and machine learning engineer, the objective is the factor that sets them apart. While the former two focus on obtaining insights from the data, an ML engineer assesses, organises and monitors data and then creates the components that can work with minimal human regulation. Although ML is a tributary of AI closely linked to Data Science, however, the skill set of research, building and running automated predictive models makes it starkly different. The knowledge of applied mathematics is integral to selecting the precise formulas for the ML algorithm. Computer science Fundamentals and Programming are other basic requisites for an ML engineer. Machine Learning Algorithm is also an important and sought-after skill. Data Modelling and Evaluation is a valuable asset that helps in providing a productive solution. Neural Language and Natural Language Programming are the fundamentals of Machine Learning.

In the coming years, AI and ML led innovations are expected to ease and assist every aspect of human lives phenomenally. This revolution is bound to redefine society and culture and will impact every industry for better with improvements in business and customer experiences. Careers in new age technologies will be much sought after and candidates specialising in these skills will be able to choose from multiple opportunities.

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