Here is the list of positions in a typical tech establishment or across organizations.

Engineering & Development

1. Junior Software Engineer / Junior Developer
2. Software Engineer / Software Developer
3. Senior Software Engineer / Senior Developer
4. Lead Software Engineer / Tech Lead
5. Principal Engineer / Staff Engineer
6. Engineering Manager
7. Senior Engineering Manager
8. Director of Engineering
9. VP of Engineering
10. Chief Technology Officer (CTO)

Database Administration & Data Engineering

1. Junior Database Administrator (Jr. DBA)
2. Database Administrator (DBA)
3. Senior Database Administrator (Sr. DBA)
4. Database Engineer
5. Data Engineer
6. Senior Data Engineer
7. Data Architect
8. Principal Data Engineer
9. Director of Data Engineering
10. VP of Data Engineering
11. Chief Data Officer (CDO) (if applicable in data-driven companies)

Product Management & UX

1. Associate Product Manager (APM)
2. Product Manager (PM)
3. Senior Product Manager (SPM)
4. Lead Product Manager
5. Principal Product Manager
6. Director of Product Management
7. VP of Product Management
8. Chief Product Officer (CPO)

Infrastructure, Cloud, and DevOps

1. Junior DevOps Engineer
2. DevOps Engineer
3. Senior DevOps Engineer
4. Cloud Engineer
5. Site Reliability Engineer (SRE)
6. Senior SRE / Lead SRE
7. Infrastructure Architect
8. Director of DevOps & Cloud Infrastructure
9. VP of Infrastructure & Cloud Operations

Security & Compliance

1. Security Analyst
2. Security Engineer
3. Senior Security Engineer
4. Security Architect
5. Director of Security
6. VP of Security
7. Chief Information Security Officer (CISO)

IT & Support

1. IT Support Specialist
2. System Administrator (SysAdmin)
3. Network Engineer
4. IT Manager
5. Director of IT
6. VP of IT
7. Chief Information Officer (CIO)

Business Intelligence & Analytics

1. Business Intelligence (BI) Analyst
2. BI Engineer
3. Data Analyst
4. Senior Data Analyst
5. Analytics Engineer
6. Director of Analytics
7. VP of Analytics

Executive & Leadership Roles

1. Vice President (VP) of Engineering/Product/Data/IT/Security
2. Chief Technology Officer (CTO)
3. Chief Information Officer (CIO)
4. Chief Data Officer (CDO)
5. Chief Product Officer (CPO)
6. Chief Executive Officer (CEO)

Now lets breakdown each combination of positions and what it will take for each position to be present and involved in todays AI worlld or face the risk of packing your bags and back to home.

1. Engineering & Development

Roles: Junior Software Engineer, Software Engineer, Senior Software Engineer, Tech Lead, Principal/Staff Engineer, Engineering Manager, Director of Engineering, VP of Engineering, CTO

Learning Path:

• Early Career (Junior to Mid-Level Engineers): Start with the Basics: Learn programming languages that are widely used in AI (like Python). Get familiar with fundamental AI concepts such as machine learning, neural networks, and data processing. Online Courses & Tutorials: Platforms like Coursera, Udacity, or edX offer beginner courses in AI and machine learning. Start small—build projects that experiment with basic algorithms. Hands-On Projects: Try integrating simple AI features into your current work, such as recommendation engines or data analysis tools.
• Advanced Roles (Senior Engineers to Managers): Deep Dive into AI Techniques: Once you’re comfortable with the basics, explore deep learning, natural language processing, and computer vision. Learn to work with frameworks like TensorFlow or PyTorch. Team Collaboration: Work closely with data scientists and specialized AI engineers. Understand how AI projects are managed, from data collection and model training to deployment. Leadership & Strategy: As you move into management, focus on how AI can improve your team’s workflow, product features, or operational efficiency. Develop a vision for integrating AI into long-term projects.

Future Outlook: In the next few years, AI will become a core part of software development. Engineers will be expected to build products with embedded AI features, and leaders will set strategies that use AI to drive innovation and competitive advantage.

2. Database Administration & Data Engineering

Roles: Junior DBA, Database Administrator, Senior DBA, Database Engineer, Data Engineer, Senior Data Engineer, Data Architect, Director/VP of Data Engineering, Chief Data Officer (CDO)

Learning Path:

• For Entry-Level & Mid-Level Roles: Understanding Data: Since AI relies on high-quality data, focus on mastering data storage, cleaning, and preprocessing techniques. Learn the Basics of Machine Learning: Understand how data is used to train models. Online courses and hands-on projects can be a great way to learn these skills.
• For Advanced Positions: Data Pipelines for AI: Learn how to build and maintain data pipelines that can support AI models. This includes working with big data technologies and cloud-based data solutions. Strategic Data Management: In leadership roles, develop strategies to ensure your organization’s data is robust, secure, and ready for advanced analytics and AI-driven insights.

Future Outlook: As AI grows, databases will not only store data but also become key components in feeding AI systems. Expect more automation in routine tasks, with DBAs shifting towards ensuring data quality and integrity for AI projects.

3. Product Management & UX

Roles: Associate Product Manager, Product Manager, Senior Product Manager, Lead Product Manager, Principal Product Manager, Director/VP of Product Management, Chief Product Officer (CPO)

Learning Path:

• Foundational Knowledge: Learn AI Concepts: Start with the basics of AI and machine learning. Understand how these technologies work and the kind of problems they can solve. User-Centered AI: Explore how AI can enhance user experience—from personalized recommendations to smarter interfaces.
• Strategic Integration: Collaboration with Tech Teams: Work with engineers and data scientists to identify opportunities where AI can add value to your product. Market & Ethical Considerations: As a product leader, stay informed about AI trends and ethical issues. This knowledge will help shape products that are both innovative and responsible.

Future Outlook: AI will be a key feature in many new products. Product managers who understand AI will be better positioned to create compelling, user-friendly products that stand out in the market.

4. Infrastructure, Cloud, and DevOps

Roles: Junior DevOps Engineer, DevOps Engineer, Senior DevOps Engineer, Cloud Engineer, Site Reliability Engineer (SRE), Infrastructure Architect, Director/VP of Infrastructure & Cloud Operations

Learning Path:

• Technical Foundations: Learn AI for Automation: Understand how AI can optimize infrastructure through predictive maintenance, automated scaling, and resource management. Familiarize with Deployment: Get hands-on experience deploying AI models in cloud environments, learning tools and platforms that support AI operations.
• Advanced Integration: System Optimization: Learn to monitor and manage AI-powered systems. Tools that leverage AI for alerting and predictive insights will become part of the standard toolkit. Security & Compliance: As AI systems are deployed at scale, understanding their security implications becomes crucial.

Future Outlook: Infrastructure roles will increasingly rely on AI to automate and optimize operations. DevOps teams will use AI for faster deployment cycles and better system performance, making these skills essential.

5. Security & Compliance

Roles: Security Analyst, Security Engineer, Senior Security Engineer, Security Architect, Director/VP of Security, Chief Information Security Officer (CISO)

Learning Path:

• Basic Awareness: Understand AI in Security: Learn how AI can be used to detect threats, analyze patterns, and automate responses. Familiarize yourself with common AI security tools. Risk Assessment: Begin by assessing how AI systems can introduce new vulnerabilities and how to mitigate these risks.
• Advanced Strategies: AI-Driven Security Tools: Gain expertise in AI-driven security platforms that help in threat detection and incident response. Policy and Ethics: Stay updated on the ethical considerations and regulatory issues surrounding AI, ensuring that security measures are robust and compliant.

Future Outlook: AI will be a double-edged sword in security—offering powerful tools to counter threats while also presenting new challenges. Security professionals will need to continuously adapt and learn to stay ahead.

6. IT & Support

Roles: IT Support Specialist, System Administrator, Network Engineer, IT Manager, Director/VP of IT, Chief Information Officer (CIO)

Learning Path:

• Practical Introduction: AI in IT Support: Learn how AI can automate routine tasks like troubleshooting, system monitoring, and user support. Familiarize yourself with AI chatbots and automated helpdesk tools. Hands-On Practice: Experiment with setting up AI-driven monitoring systems and basic automation scripts.
• Strategic Implementation: Manage AI Systems: For leadership roles, focus on integrating AI tools that enhance system reliability and efficiency while ensuring the team is well-prepared to manage these new technologies. Change Management: Learn how to guide your team through the transition to more AI-centric IT operations.

Future Outlook: AI will streamline many routine IT tasks, allowing support teams to focus on more complex issues. Leaders will need to balance human oversight with AI automation to ensure smooth operations.

7. Business Intelligence & Analytics

Roles: BI Analyst, BI Engineer, Data Analyst, Senior Data Analyst, Analytics Engineer, Director/VP of Analytics

Learning Path:

• Skill Building: Learn AI Tools: Get comfortable with AI-driven analytics platforms and learn how machine learning models can improve data interpretation. Data Storytelling: Use AI to uncover insights from data and learn how to communicate these insights effectively to non-technical stakeholders.
• Advanced Analysis: Predictive & Prescriptive Analytics: Move beyond descriptive analytics by incorporating predictive models that can forecast trends and prescribe actions. Integrate AI in Decision Making: Learn how to set up systems where AI plays a key role in supporting business decisions.

Future Outlook: Analytics roles will transform as AI provides deeper, more accurate insights. The ability to harness AI will be crucial for making informed decisions in a fast-paced business environment.

8. Executive & Leadership Roles

Roles: VP of Engineering/Product/Data/IT/Security, CTO, CIO, CDO, CPO, CEO

Learning Path:

• Strategic Learning: Stay Informed: Leaders should familiarize themselves with AI trends, challenges, and opportunities. This doesn’t mean becoming an AI expert, but understanding enough to make strategic decisions. Workshops & Seminars: Attend industry events, workshops, and seminars focused on AI to network with experts and gain insights into how AI is reshaping business models.
• Driving Transformation: Foster a Culture of Learning: Encourage ongoing AI education across your teams. Support initiatives that integrate AI into your organization’s strategy. Ethics & Governance: Understand the ethical implications of AI and lead discussions on responsible AI use, ensuring your organization’s practices are fair and transparent.

Future Outlook: Executives will play a key role in guiding AI adoption across organizations. They will set visions that incorporate AI as a critical tool for growth, innovation, and competitive advantage while navigating the ethical and regulatory landscape.

In Summary

For Everyone:

NOTE: The future positions will be combination of jobs for single positions. So data engineering is combined with AI Engineering OR UX is combined with Development. Either way know more does not hurt.

• Continuous Learning: AI is a rapidly evolving field. No matter where you stand, commit to continuous learning through online courses, certifications, and real-world projects.
• Collaboration is Key: Work closely with teams that have specialized AI skills. Cross-functional collaboration will be crucial in the coming years.
• Adaptability: The integration of AI will change traditional roles. Embrace change and be ready to adapt your skills to new challenges and opportunities.

The Big Picture: Over the next few years, AI will become a fundamental part of nearly every job in tech. From automating routine tasks to creating new products and services, AI will redefine roles across the board. Whether you’re writing code, managing data, or setting company strategy, a solid understanding of AI will help you stay relevant and drive innovation in your organization.

By taking a step-by-step approach to learning AI—tailored to your current role—you’ll be well-prepared to navigate and lead through this exciting transformation in the tech industry.