As technology rapidly evolves, the way businesses operate is undergoing a constant change, necessitating professionals to continuously upskill. Artificial intelligence, in particular, is revolutionising industries at an unprecedented pace. According to LinkedIn data, by 2030, 70% of job-related skills will be different from what they are today. For engineers looking to future-proof their careers, staying ahead of these changes is ever more necessary.
LinkedIn’s recent report, Skills on the Rise 2025, has identified the fastest-growing engineering skills in India. Whether you’re an aspiring engineer, a seasoned professional looking to pivot, or simply interested in staying relevant in a tech-driven world, the list of skills shows the most promising upskilling investments for 2025 and beyond:
Large Language Models (LLM)
Leading the list is the development and application of Large Language Models (LLM). These AI-driven models, like GPT and BERT, have revolutionized natural language processing, powering chatbots, search engines, and content generation tools. Learning LLM development involves understanding neural networks, training massive datasets, and fine-tuning models for specific tasks. Engineers diving into this field will gain expertise in deep learning frameworks, data preprocessing, and model optimization, key skills for the AI-driven future.
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Software Design
Software design remains a cornerstone of engineering expertise. As applications become more complex and user demands more sophisticated, the ability to architect scalable, maintainable, and efficient systems is critical. A course in software design typically covers design patterns, system architecture principles, and object-oriented programming.
Prompt Engineering
With the rise of AI-powered tools, prompt engineering has emerged as a crucial skill. It involves crafting precise inputs to guide AI models in generating the most relevant and useful outputs. This discipline requires an understanding of AI behavior, linguistic structures, and data input techniques. Learning prompt engineering equips engineers with the ability to optimize AI responses for applications in customer service, content creation, and automation.
Technical Documentation
Engineering is not just about building systems, it’s also about explaining them clearly. Technical documentation is an essential skill that ensures clarity in product development, API integration, and software maintenance. Courses in this domain focus on best practices for writing manuals, API documentation, and internal guides. Engineers who master this skill enhance collaboration across teams and improve user experience.
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Code Review
Code review is an integral part of software development, ensuring code quality, security, and efficiency. Engineers skilled in code review are adept at identifying vulnerabilities, enforcing coding standards, and fostering collaboration within development teams. Learning this skill involves understanding best practices, automated review tools, and common pitfalls in programming, making it invaluable for both junior and senior developers.
PyTorch
PyTorch has gained immense popularity as a deep learning framework, making it a must-learn skill for AI and machine learning engineers. It enables rapid prototyping, model training, and neural network development. A course in PyTorch typically covers tensor operations, model building, backpropagation, and real-world AI applications. Engineers mastering PyTorch can contribute to cutting-edge AI research and industrial implementations.
Optimization
Optimization is a critical skill across various engineering disciplines, from machine learning to operations research. Engineers skilled in optimization can improve system performance, reduce computation costs, and enhance efficiency in AI models. Training in this field includes mathematical optimization techniques, algorithm tuning, and real-world problem-solving applications.
AI Literacy
AI literacy is no longer optional for engineers—it’s a necessity. This skill encompasses an understanding of how AI works, its ethical implications, and its practical applications in different industries. Engineers learning AI literacy gain insight into model interpretability, bias mitigation, and regulatory considerations, making them well-equipped to work responsibly with AI systems.
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Systems Design
Systems design is a crucial skill for engineers building large-scale applications and distributed systems. It involves designing software architecture, managing scalability, and ensuring fault tolerance. A systems design course covers microservices, API development, and cloud computing strategies. Engineers proficient in this area are better prepared for high-level technical roles that require strategic thinking and problem-solving.
Jupyter
Jupyter notebooks have become an indispensable tool for engineers working in data science, AI, and research. This interactive environment allows for seamless coding, visualization, and documentation in Python. Learning Jupyter equips engineers with the ability to efficiently test models, analyze data, and collaborate on projects, making it a key skill for data-driven roles.
How these skills were identified as ‘fastest-growing’?
LinkedIn analyzed unique data trends from professionals in engineering roles to determine these top skills. The study measured the fastest-growing skills based on how frequently professionals were adding them to their profiles and how actively companies were seeking these skills in job postings.
The resulting list highlights the most in-demand technical competencies in the Indian job market, offering a clear roadmap for engineers looking to upskill and stay ahead in the ever-evolving industry.
