How to become An AI Engineer

• AI Architect
• AI Ethicist
• AI Interaction Designer
• AI Product Engineer
• AI Technology Software Engineer
• Big Data Engineer
• Big Data Architect
• Business Intelligence Developer
• Computer Vision Engineer
• Data Scientist
• Machine Learning Engineer
• Research Scientist
• Robotics Engineer

What do AI Engineers do?

An AI Engineer would typically need to:

• Build AI models from scratch to meet the various objectives of their organisation; convert machine learning models into application program interfaces (APIs) that can be reapplied
• Develop and automate data ingestion and data transformation infrastructure; use data cleaning to confirm data quality; supervise the process of additional data acquisition 
• Study the machine learning algorithms that can resolve a specific issue; rank the algorithms by their success probability
• Conduct statistical analysis, data extrapolation, statistical modelling and evaluation strategies to help their organisation make more educated decisions
• Identify, analyse and design strategies to fix errors and bugs in models; build metrics that track the progress of AI models even with changing business targets
• Understand and implement fundamentals of computer science and mathematics to develop AI infrastructure and data pipelines so that the code and the model come to life
• Work with data engineers and stakeholders to deploy the models to production after establishing consensus
• Define validation strategies, preprocessing or feature engineering is done on a given dataset and data augmentation pipelines
• Explore, visualise and understand the data to foresee how differences in data distribution could affect performance once deployed
• Optimise existing resources such as hardware, data and labour to meet deadlines
• Be able to explain the hi-tech processes to non-specialists in non-technical languages

AI Engineers work in an office-based environment, for the most part. Remote work is a definite possibility, especially when you take up contracts on a part-time or freelance basis or when you must collaborate with other engineers or research scientists in different locations.

AI Engineers usually work 40 hours a week from 9 a.m to 5 p.m, Monday to Friday, except when they need to put in extra hours to meet project deadlines. Time off is negotiable, and holiday options are pretty attractive. 

Finding a new job might seem challenging. AI Engineers can boost their job search by asking their network for referrals, contacting companies directly, using job search platforms, going to job fairs, and inquiring at staffing agencies.

AI Engineers are generally employed by:

• Government Agencies
• Computer Systems Design & Related Services
• Physical, Engineering & Life Science Research and Development Entities 
• Software Publishers
• Education Industry
• Health Care Industry
• Finance
• Manufacturing Industry
• The Corporate Sector
• Enterprises

Professional associations and organisations, such as the International Society of Applied Intelligence, are crucial for an AI Engineer interested in pursuing professional development or connecting with like-minded professionals in their industry or occupation.

Membership in one or more adds value to your resume while bolstering your credentials and qualifications.

• High probability of making errors while analysing and using data; technical bugs and errors that repeatedly occur in the final stages of testing
• Acclimatising to a professional working environment by understanding the difference between academic education and professional demands
• Ensuring the customer and other team members are on the same page regarding one’s role in the project
• Keeping up with the changing trends of technology fuelled by intense competition and globalisation
• Long hours spent at the computer may induce chronic health issues; multiple health issues caused by disrupted circadian rhythms and sleep patterns due to frequent overnight work
• Lack of work-life balance along with stresses involved in handling various projects and additional responsibilities
• Lack of a standard set of proper rules to prevent the misappropriation of public data

Any academic program that a potential AI Engineer takes up typically requires a period of supervised experience, such as an internship or placement, which will teach you valuable coding and programming skills.

Since the role is relatively new, aspirants can enjoy some flexibility in attaining it. So, if your education provider cannot offer an internship or industry placement, be proactive and strive to master these skills on your own time.

It's also possible that prospective employers may accept desirable educational qualifications and closely related experience in place of an internship or placement. AI Engineers who wish to be elevated to higher positions must spend at least five to ten years acquiring significant expertise in multiple programming languages.  

Read about the profession and interview/job shadow experts working in AI to prove your commitment to course providers and prospective employers.

AI is an emerging field, and there are relatively fewer courses available for in-depth specialisation. An undergraduate degree in computer science, IT, cognitive science, linguistics, electrical engineering, robotics, physical sciences, statistics, applied mathematics, finance, or economics is the first step towards becoming a qualified AI Engineer.

Since most employers prefer more knowledgeable candidates with a Masters degree or a PhD, you must obtain either one of them in any of the subjects mentioned above, which includes a significant element of machine learning and AI.

Entry without a degree is rare and is viable only for desirably skilled applicants who have completed specific courses. Check with education providers and prospective employers for more precise details before embarking on this course.

Build a strong foundation of the basics by focusing on physics, computer science, mathematics and economics in high school.

Certification demonstrates an AI Engineer’s competence in data science, robotics, machine learning, biomedical research or AI. A combination of education, experience, and testing is generally required to gain certification, though requirements differ from region to region. 

Certification from an objective and reputed organisation can help you stand out in a competitive job market, carry a significant salary premium of up to 18 per cent and increase your chances of advancement.

AI offers ample opportunities for professional growth across various industries but especially so in large multinational tech companies. 

AI Engineers with considerable experience and high-performance levels can expect timely promotions to leadership positions that involve team management or become Computer & Information System Managers.

The prevailing low level of competition in the industry should work in your favour if you wish to form your own company immediately after graduation. 

Candidates with masters or doctoral degrees, appropriate certification and machine learning engineering skills have the best job prospects.

Continuing professional development (CPD) will help an active AI Engineer build personal skills and proficiency through work-based learning, a professional activity, formal education, or self-directed learning. It allows you to upskill continually, regardless of your age, job, or level of knowledge.

Continuing education is essential to update your skills and thrive in the constantly evolving IT industry. Often, leading organisations offer their AI Engineers in-house training courses. Elsewhere, the employee might have to take the initiative and complete specific application, language or operating system courses.

A nanodegree in advanced machine learning engineering will prove worth the while, as would additional qualifications in areas such as leadership and management.

A doctoral degree program in AI will give its students deep insight into the design and analysis of algorithms and data structures, processing big data and solving complex problems. Since these programs are relatively rare, pursue one only if you are sure about a career that involves research and teaching at the university level.

What makes AI so Popular?

Companies that seek to automate and optimise processes or produce actionable information need AI systems to extract value from data. Tasks beyond a single person but well within the bounds of AI systems powered by machine learning deliver clear communication, predicting critical care events, and identifying potentially fraudulent transactions.

What Can You Do with an AI Degree?

Graduates of artificial intelligence have developed AI-based systems that impact various sectors, from agriculture to gaming.Agriculture

This sector uses AI-based drones and tractors, weather monitoring systems and robots to harvest crops, detect soil defects and nutrient deficiencies, and identify where weeds grow. 

Automotive

AI-based vehicles have self-driving facilities and are equipped with emergency braking, blind-spot monitoring, driver-assist steering and predictive vehicle maintenance. 

Construction

AI can predict and prevent cost overruns based on project size, contract type, and project manager competence. 3D building information modelling assists in the efficient planning, design, construction, and management of buildings. Self-driving construction machinery performs repetitive tasks such as pouring concrete, bricklaying and demolition.

Education

AI has the power to automate administrative tasks associated with education. AI-based design and digital platforms provide learning, testing, feedback to students and real-time translation of what a teacher is saying. 

Gaming

The gaming industry has created intelligent human-like non-player characters (NPCs) interacting with players to predict human behaviour and lead to better game design and testing. 

Healthcare

In addition to detecting diseases and monitoring patients, AI can analyse chronic conditions and relevant medical data that may help with the discovery and invention of new drugs. AI also helps carry goods and clean medical facilities and equipment.

Human Resources

Intelligent software can examine applications and resumes based on specific parameters, check references and scores and automate labour-intensive assembly lines in manufacturing industries. 

Marketing / Retail / E-Commerce

Personalised shopping recommendation engines can analyse the customer’s browsing history and interests and connect them with appropriate marketing campaigns, conversational marketing bots and potential purchases. 

Human sounding AI-powered virtual shopping assistants and chatbots also enhance the customer’s online shopping experience. Its success in reducing credit card fraud by analysing usage patterns and fake customer reviews further endears AI worldwide.

AI Jargon Simplified

Machine learning is an AI application that allows systems to learn and improve from experience without being explicitly programmed automatically. It develops computer programs that can access data and use it to learn by themselves.

Application program interfaces (APIs) are a set of functions and procedures that enable applications that access data and features of other applications, services, or operating systems.

Data ingestion is the movement of data from various sources to a storage medium where it may be accessed, used, and analysed by an organisation. The destination is typically a data warehouse, data mart, database, or document house.

Data Acquisition or DAQ is the process of digitising data from the world around us to be displayed, analysed and stored in a computer. E.g. measuring the temperature in a room as a digital value using a sensor such as a thermocouple. Modern DAQ systems can include data analysis & reporting software, network connectivity, and remote control & monitoring options. 

Data extrapolation takes facts and observations about a present or known situation and uses them to predict what might eventually happen. Techniques include statistical modelling and validation strategies.

Statistical modelling is the process of applying statistical analysis to a dataset. A statistical model is a mathematical representation or mathematical model of observed data to approximate reality and make predictions from this approximation.

Validation Strategy includes cross-referencing the functionality of software with the requirement specification to assess that it adheres to the prescribed demands of the client.

The latter carries out cross-referencing the functionality of software with the requirement specification to assess that it adheres to the prescribed demands of the client.

Data augmentation refers to techniques used to increase data by adding slightly modified copies of existing data or creating new synthetic data from existing data.

Potential Pros & Cons of Freelancing vs Full-Time Employment

Freelancing AI Engineers have more flexible work schedules and locations. They have full ownership of the business and can select their projects and clients. However, they experience inconsistent work and cash flow, which means more responsibility, effort and risk.

On the other hand, a full-time AI Engineer has company-sponsored health benefits, insurance, and retirement plans. They have job security with a fixed, reliable source of income and guidance from their bosses. Yet, they may experience boredom due to a lack of flexibility, ownership, and variety.

When deciding between freelancing or being a full-time employee, consider the pros and cons to see what works best for you.

AI-driven pets, which are said to enter the market by 2025, will look, feel and act like real pets but will not pose any of the issues real pets do. 

A marked partiality for math and computers, a keen passion for innovation and problem-solving, along with a noble predisposition to help the planet thrive are characteristics that set individuals on the path to becoming AI Engineers.

Be a good team player. AI is an emerging field, and no one person can accelerate its growth. Try to find ways to engage with AI outside your workplace via social media, marketing, sports and more. There is endless scope for the application of AI and machine learning techniques.