Example<\/strong>: The AI Engineer can develop a customer support chatbot for the bank that can understand the queries of the customer and respond to them appropriately. The application can also assist the customers to view their transactions and obtain information about their banking services.<\/p>\nSkills required: <\/strong>Python, machine learning, deep learning, APIs, data structures and software development.<\/p>\n<\/span>2. Machine Learning (ML) Engineer<\/span><\/h3>\nML engineers are professionals who create programs and algorithms that can learn independently from what tasks they have to do. ML engineers create machine learning models to assist companies to predict future results.<\/p>\n
\n- Creation and training of machine learning models.<\/li>\n
- Data preprocessing and analysis.<\/li>\n
- Selection of suitable algorithms.<\/li>\n
- Model evaluation and improvement.<\/li>\n
- Deployment of the models.<\/li>\n<\/ul>\n
Example:<\/strong> An ML Engineer at an e-commerce company could build a product recommendation system. The system uses customers\u2019 browsing history, previous purchases and interests to suggest products they might want to buy.<\/p>\nSkills required:<\/strong> Python, statistics, linear algebra, machine learning algorithms, TensorFlow or PyTorch, and data processing.<\/p>\n<\/span>3. Generative AI Engineer<\/span><\/h3>\nGenerative AI Engineers build applications that leverage AI models to generate new content, answer questions, and execute tasks. These apps can generate text, images, code, summaries and other types of content.<\/p>\n
As Large Language Models (LLMs) rise, companies are looking for engineers who can build real-world applications with models and AI tools.<\/p>\n
\n- Development of AI chatbots and virtual assistants.<\/li>\n
- Application development with large language models.<\/li>\n
- Constructing Retrieval-Augmented Generation (RAG) systems.<\/li>\n
- Developing AI agents capable of carrying out multiple tasks.<\/li>\n
- Tailoring models to fit specific business requirements.<\/li>\n<\/ul>\n
Example:<\/strong> A Generative AI Engineer may develop an AI assistant for an educational platform. Students can upload their study materials and ask questions on them. The assistant is able to extract relevant information from the uploaded documents and produce answers.<\/p>\nMandatory skills:<\/strong> Python, LLMs, prompt engineering, RAG, vector databases, APIs and AI application development.<\/p>\n<\/span>4. Data Scientist<\/span><\/h3>\nThe role of Data Scientists involves analyzing huge amounts of data, identifying patterns and business issues as well as making decisions in relation to this. Data Scientists employ different methods of statistics and programming to convert data into information. This profession suits people who enjoy working with numbers and analysis.<\/p>\n
\n- Collecting and cleaning large datasets<\/li>\n
- Pattern detection and trend identification<\/li>\n
- Building predictive models<\/li>\n
- Statistical analysis of data<\/li>\n
- Data visualization<\/li>\n<\/ul>\n
Example: <\/strong>A Data Scientist at a bank analyzes the data about transactions to detect abnormal behavior patterns. Machine learning models can help them find potential frauds.<\/p>\nSkills required:<\/strong> Python, SQL, statistics, probability, data visualisation, machine learning and data analysis.<\/p>\n<\/span>5. Software Development Engineer (SDE)<\/span><\/h3>\nStudents enrolled in the course of Applied AI Engineering can also delve into careers in conventional software engineering. Not all the jobs in the realm of technology involve developing machine learning algorithms. There are also engineers who build software, platforms, and back-end infrastructure for the deployment of AI solutions.<\/p>\n
\n- Web and Mobile app development.<\/li>\n
- Back end development and APIs.<\/li>\n
- Software Architecture Design<\/li>\n
- Integrating AI models and other applications.<\/li>\n
- Software testing and maintenance<\/li>\n<\/ul>\n
Example:<\/strong> A software development engineer can build a web application for e-commerce and can use AI based product recommendations in the app based on customer behaviour.<\/p>\nSkills Required:<\/strong> Python, Java, C++, JavaScript, data structures and algorithms, databases, system design, and software testing.<\/p>\n<\/span>6. ML Platform Engineer\/MLOps Engineer<\/span><\/h3>\nMachine learning modeling alone cannot be considered a complete process within an AI project as there also should be experts that will help to deploy models, monitor their performance, and ensure that they keep performing well.<\/p>\n
ML Platform and MLOps Engineers are responsible for setting up the infrastructure and processes related to managing machine learning systems across their full lifecycle by automating the processes of training, deploying, testing, and monitoring ML models.<\/p>\n
\n- Creating ML deployment pipelines<\/li>\n
- Automating ML training and testing<\/li>\n
- Managing cloud infrastructure of AI applications<\/li>\n
- Model monitoring and error detection<\/li>\n
- Scalability and reliability of AI systems<\/li>\n<\/ul>\n
Example:<\/strong> MLOps Engineer for a fintech company can set up a process of deploying a fraud detection model, monitor its performance, and initiate retraining of a model when the latter starts producing less accurate results.<\/p>\nSkills needed:<\/strong> Python, cloud computing, Docker, Kubernetes, CI\/CD, machine learning, ML deployment and monitoring.<\/p>\n<\/span>7. Computer Vision Engineer<\/span><\/h3>\nThe Computer Vision Engineers design intelligence algorithms that have the ability to both see and understand whatever it is that they are viewing. These professionals utilize machine learning and deep learning techniques to train computers to recognize objects, identify faces, detect patterns and examine visual data such as pictures and videos. The main task of these professionals is to train computers to ‘see’ like humans do. They assist computers in understanding information. Their main focus area is training computers to identify objects, recognize people, detect patterns and analyze images and videos.<\/p>\n
\n- Building recognition of images technology.<\/li>\n
- Developing object detection technologies.<\/li>\n
- Creating facial recognition and image classification algorithms.<\/li>\n
- Technology of AI to analyse video data.<\/li>\n
- Development of visual inspection technology for makers.<\/li>\n<\/ul>\n
Example:<\/strong> A computer vision engineer might develop an automated quality inspection system for a manufacturer. The cameras take pictures of the product moving down the assembly line and the AI algorithm detects any defects.<\/p>\nSkills required: <\/strong>Python, OpenCV, computer vision, image processing, deep learning and PyTorch or TensorFlow.<\/p>\n<\/span>8. AI Product Engineer<\/span><\/h3>\nThe AI Product Engineers are well versed with software engineering as well as AI and are able to develop digital products specific to certain user problems. These engineers take part in all stages of product development, including problem solving, developing, testing, and improving upon it. Whereas some experts solely focus on the training of the model, the AI Product Engineers do much more than that.<\/p>\n
\n- Creating digital products that use AI.<\/li>\n
- Using LLMs and machine learning models in apps.<\/li>\n
- Creating user-friendly AI capabilities.<\/li>\n
- Testing the product\u2019s functionality and user experience.<\/li>\n
- Enhancing the app according to the user feedback.<\/li>\n<\/ul>\n
Example: <\/strong>An AI Product Engineer creates an AI-powered platform for the screening of resumes. The platform is able to analyze job offers, read candidate’s resumes and detect their skillsets.<\/p>\nSkills required: <\/strong>Software development, Python, APIs, Generative AI, frontend and backend development, system design and product thinking.<\/p>\n<\/span>9. Applied AI Researcher<\/span><\/h3>\nApplied AI Research can be very good for those who want to dive deeper into sophisticated technologies and solve difficult technical challenges.<\/p>\n
Applied AI Researchers analyse existing AI approaches, try out different models and find out solutions that allow them to improve efficiency of conventional artificial intelligence systems. Most of the time their research is orientated toward solving some practical problems rather than being purely theoretical in nature.<\/p>\n
\n- Experimenting with machine learning and deep learning models<\/li>\n
- Investigating novel AI architectures and methods.<\/li>\n
- Improving model accuracy and efficiency.<\/li>\n
- Learning natural language processing & computer vision.<\/li>\n
- Build and validate new AI applications.<\/li>\n<\/ul>\n
Example:<\/strong> An Applied AI Researcher can look for solutions that will enhance the accuracy of language models in answering medical-related questions.<\/p>\nSkills required: <\/strong>Mathematics, statistics, Python, machine learning, deep learning, research methods and scientific problem-solving.<\/p>\n<\/span>Industries That Hire Applied AI Engineering Graduates<\/strong><\/span><\/h2>\nNot technology companies use AI. There are different areas where companies use AI to improve the way they provide services to automate tasks and to make better decisions. This creates job opportunities for students who study Applied AI Engineering.<\/p>\n
\n- Healthcare – Building AI models that look at pictures handle patient information find diseases and help patients with their health needs.<\/li>\n
- Banking and Finance – Building AI systems that find fraud manage risks predict money-related results and automate help for customers.<\/li>\n
- E-commerce and Retail – Building AI systems that suggest products predict what customers might want predict what will be needed and manage stock.<\/li>\n
- Automobile and Manufacturing – Building systems that drive cars on their own check products visually predict when machines might need work and automate factories.<\/li>\n<\/ol>\n
5.. Ed-Tech – Building AI teachers, systems that adapt to each student, automatic tests and smart help for questions.<\/p>\n
\n- IT and Software Services – Building AI programs, tools that automate work, smart software platforms and AI services, in the cloud.<\/li>\n<\/ol>\n
<\/span>How Does Polaris School of Technology Prepare Students for These Career Paths?<\/strong><\/span><\/h2>\nA career in AI involves not only a basic understanding of AI but the technical skills as well in the form of programming and the creation of applications to solve real-world problems.<\/p>\n
The Polaris School of Technology operates on a four-year framework of education that slowly introduces students to software engineering and AI technology.<\/p>\n
\n
\n\n\nYear<\/strong><\/td>\nLearning stage<\/strong><\/td>\nMajor areas covered<\/strong><\/td>\n<\/tr>\n\n| Year 1<\/td>\n | Seed<\/td>\n | Programming fundamentals, data structures and algorithms, mathematics for AI\/ML, databases and frontend development<\/td>\n<\/tr>\n | \n| Year 2<\/td>\n | Grow<\/td>\n | Data science, full-stack development, Generative AI architectures, system design and deep learning<\/td>\n<\/tr>\n | \n| Year 3<\/td>\n | Disrupt<\/td>\n | LLM engineering, AI infrastructure, MLOps, Generative AI and responsible AI<\/td>\n<\/tr>\n | \n| Year 4<\/td>\n | Launch<\/td>\n | Industry immersion, production ML systems, open-source contributions and capstone projects<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/span>Is Applied AI Engineering a Good Career Choice?<\/strong><\/span><\/h2>\nApplied AI Engineering can make for a suitable career option for those who like programming, mathematics, technologies and problem-solving. It provides scope in Machine Learning, Generative AI, Data Science, Software Development, Computer Vision and AI Infrastructure. Applications like AI chatbots, recommendation systems, automated software, etc., can be developed by the students.<\/p>\n Four-year Bachelor of Technology program offered by Polaris School of Technology combines computer sciences with advanced AI courses along with projects, exposure to Microsoft Azure and industry-relevant training. This may help in developing the necessary skills for various positions in the technology industry. Before applying to the program, the students need to verify degree recognition, cost of tuition, full syllabus, internships and placements records. Degree in AI will act as a base, but career advancement also requires practical skills and performance in interviews.<\/p>\n <\/span>Build a Future-Ready Career with Applied AI Engineering at Polaris<\/strong><\/span><\/h2>\nPolaris School of Technology\u2019s Applied AI Engineering is a career program in Machine Learning, Generative AI, Software Development and Data Science. The curriculum merges the basics of computer science, state-of-the-art AI concepts, and hands-on projects to develop industry-relevant skills. The career growth depends on the students\u2019 specialisation, practical experience and constant learning.<\/p>\n <\/span>Career Paths After Studying Applied AI Engineering at Polaris FAQs<\/strong><\/span><\/h2>\n\n- What kinds of job positions can the graduates of the Applied AI Engineering program take in Polaris?<\/li>\n<\/ol>\n
Students with different skill sets and experience can choose from several job positions including but not limited to such job positions as AI Engineer, Machine Learning Engineer, Generative AI Engineer, Software Development Engineer, Data Scientist, MLOps Engineer, AI Product Engineer and Applied AI Researcher.<\/p>\n \n- Can students graduate as software engineers?<\/li>\n<\/ol>\n
As part of the curriculum, there is programming basics, data structures & algorithms, full stack development, and system design which can serve as preparation for becoming software engineers.<\/p>\n \n- Will graduates be able to find themselves in the Generative AI industry after the program?<\/li>\n<\/ol>\n
Generative artificial intelligence systems, LLM engineering, AI agents, and RAG-based applications are covered in the curriculum which can be used to develop generative applications.<\/p>\n \n- Does the Polaris School of Technology offer hands-on AI projects?<\/li>\n<\/ol>\n
The Applied AI program features projects such as sentiment analysis APIs, image classification applications, RAG powered chatbots, AI agents and production machine learning systems.<\/p>\n \n- Can graduates of Applied AI Engineering go for higher studies?<\/li>\n<\/ol>\n
Yes, graduates can join post graduate programs such as MTech, MS or specialised master\u2019s degree in Artificial Intelligence, Machine Learning, Data Science or Computer Science, if they meet the admission criteria of the institute.<\/p>\n","protected":false},"excerpt":{"rendered":" The Artificial Intelligence is creating new career paths in healthcare, banking, education, e-commerce, manufacturing and software development. 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