AI Engineer: Scope, job description, skills, salary, and more

In the fast-paced world of technology, AI engineering is a young and promising field with great prospects and importance. AI engineers are one of the key drivers in creating intelligent systems that utilize natural language processing and computer vision to enhance human capabilities and reinvent operations.

To succeed in this domain, you must have a diverse set of skills, which entails mastery of programming languages such as Python, competence in machine learning frameworks, and a firm understanding of mathematics and statistics. Good problem-solving skills and the ability to handle a large amount of data are a must as well.

The demand for AI engineers is soaring; they get paid from 100,000 to 150,000 US dollars annually, with their experience and location playing a role. AI will keep transforming businesses as people learn about this discipline, with new doors open to exciting careers and possibilities to design the future of technology.

Also read: Interesting predictions on data science trends for 2024 – Online Manipal

Understanding the Scope of AI Engineering

Engineering AI implies developing, creating, and using artificial intelligence systems and solutions to automate tasks and solve challenges in different areas.

• In healthcare, AI engineering is employed for medical image analysis, disease diagnosis, drug discovery, and personalized treatment suggestions, which contribute to better care delivery and patient outcomes.
• In finance, AI enables algorithmic trading, fraud detection, customer service chatbots, and risk management systems.
• In the automotive industry, AI engineering is applied in self-driving cars, predictive maintenance, and vehicle safety, thus paving the way for smart transportation of the new age.

The significance of AI engineering is in its ability to spark innovation across industries through data analytics, automation, and the application of intelligent algorithms. AI makes it possible for organizations to capitalize on its power, increase efficiency, reduce expenses, and achieve a competitive advantage in a quickly changing market environment.

Job Description of an AI Engineer

• Developing AI Models: AI engineers design and develop machine learning and deep learning models tailored to specific use cases, such as natural language processing, computer vision, or predictive analytics.
• Implementing Algorithms: AI engineers translate algorithms and models into production-ready software solutions. This involves writing code, leveraging frameworks like TensorFlow or PyTorch, and integrating AI components into existing systems or applications.
• Data Preparation and Analysis: AI engineers work closely with data scientists to preprocess and analyze data, ensuring its quality, relevance, and suitability for training AI models. They may also be involved in feature engineering, dimensionality reduction, and data augmentation tasks.
• Collaboration with Cross-Functional Teams: AI engineers collaborate with cross-functional teams comprising data scientists, software engineers, domain experts, and stakeholders. They participate in brainstorming sessions, data gathering, and solution design discussions to ensure alignment with business objectives and technical feasibility.

Also read: Unleashing the potential of predictive analytics in data science – Online Manipal

Skills Required for AI Engineers

Educational Background and Training

• Computer Science or Related Degree: Many AI engineers hold a bachelor’s or master’s degree in computer science, artificial intelligence, machine learning, or a related field.
• Mathematics and Statistics: A solid understanding of mathematics, including calculus, linear algebra, probability, and statistics, is essential for AI engineering.
• Certifications and Courses: Taking specialized courses and obtaining certifications in artificial intelligence, machine learning, and deep learning can enhance an individual’s skills and knowledge in the field. Online platforms like Coursera, Udacity, and edX offer a wide range of AI-related courses and certification programs.

Also read: Top 10 things recruiters look for in Data Science aspirants – Online Manipal

Salary Trends for AI Engineers

Factors influencing AI engineer salaries:

• Experience: Experienced AI engineers with several years of hands-on experience typically command higher salaries than entry-level professionals.
• Skills and Expertise: Proficiency in in-demand programming languages, machine learning frameworks, and specialized AI techniques can positively impact salary levels.
• Education: Higher education degrees, such as master’s or doctoral degrees in AI-related fields, may lead to higher salary offers.

In the United States, the average annual salary for an AI engineer exceeds 110,000 US dollars. Across the border in Canada, AI engineers can expect an average annual salary of over 85,000 Canadian dollars. Similarly, in Europe, particularly in the UK, the median annual salary for AI engineers is impressively over 60,000 pounds.

Source

• Entry-level (0-2 years of experience): INR 4-8 lakhs per annum
• Mid-level (2-5 years of experience): INR 8-15 lakhs per annum
• Experienced (5-10 years of experience): INR 15-30 lakhs per annum
• Senior-level (10+ years of experience): INR 30+ lakhs per annum

Career Growth and Opportunities in AI Engineering

AI engineers can choose to specialize in various subfields, including computer vision, natural language processing, robotics, reinforcement learning, and more. 

Emerging trends shaping the future of AI engineering:

• Explainable AI: There’s a growing emphasis on developing AI systems that are transparent and interpretable, allowing users to understand the reasoning behind AI decisions.
• Ethical AI: With increased concerns about bias, fairness, and accountability in AI systems, there’s a growing demand for AI engineers skilled in designing and implementing ethically responsible AI solutions.

Also read: What is the difference between PGCP data science and MSc data science? – Online Manipal

Challenges and Ethical Considerations

Ethical dilemmas abound in AI development and deployment. AI algorithms may inadvertently perpetuate biases in training data, leading to unfair decisions or discrimination. Moreover, AI systems’ opacity raises concerns about accountability and transparency.

AI engineers can mitigate bias by carefully selecting and preprocessing training data, employing fairness-aware algorithms, and conducting thorough testing and validation. Transparency can be enhanced through model interpretability techniques, allowing stakeholders to understand how AI decisions are made.

How can MAHE help you realize your AI dream?

Manipal Academy of Higher Education (MAHE) offers an MSc Data Science program tailored to aspiring AI engineers. The MSc Data Science program at MAHE provides a comprehensive curriculum covering fundamental concepts in data science, machine learning, and AI. Students gain proficiency in programming languages, statistical analysis, and advanced machine-learning techniques. The program emphasizes ethical considerations, equipping students with the knowledge and skills to navigate the ethical challenges inherent in AI development and deployment.

Conclusion

In conclusion, the scope for AI engineering presents immense opportunities for innovation and societal impact, but it also poses significant ethical challenges. AI engineers play a pivotal role in addressing these challenges by promoting fairness, transparency, and accountability in AI systems. With the support of institutions like MAHE and programs like the MSc Data Science, aspiring AI engineers can acquire the skills and knowledge needed to realize their AI dreams.