1. Artificial Intelligence (AI) and Data Science
1. Generative AI - Prompt engineering, model fine-tuning, and integrating GenAI into enterprise applications.
2. Machine Learning (ML) & Deep Learning -
3. Data Engineering - Working with Big Data technologies like HDFS, Spark, Kafka, and Python for data pipelines.
4. Data Analytics - Extracting insights, business intelligence, and building reporting dashboards.
Cloud and Infrastructure
1. Cloud Computing (AWS, Azure, GCP) - CI/CD pipelines, Kubernetes (K8S), Docker, Ansible, and Cloud Foundry for rapid deployment and quality engineering.
2. DevOps & Enterprise Agile
3. Cybersecurity - Assurance and protection of networks, applications, and cloud environments.
4. Microservices & Full Stack - Java, Spring Boot, .NET, and other modern development frameworks for building scalable microservices architectures.
Core IT & Digital Fundamentals
1. Software Development - Strong fundamentals in Data Structures and Algorithms (DSA), and core languages like Java, Python, and C++.
2. Testing & Quality Engineering - QA Automation (e.g., Selenium), Performance Testing (JMeter/LoadRunner), and specialized testing roles.
3. Database & System Administration - SQL, PostgreSQL, and other database management skills, alongside System Engineering.