#YourCareer : Tech Jobs Aren’t Disappearing — The Roles In Demand Are Changing. These Tech Jobs Aren’t Going Anywhere Just Yet. Great REad!

1. Software Developers

When AI can write code, what makes an engineer worth hiring? For years, developers built their careers partly on their ability to turn customer requirements into working software. Now, producing that first version is becoming easier, while deciding whether it solves the right problem, fits the surrounding system and can be trusted still requires expertise. Good engineers have always needed judgment. What is changing is the responsibility that comes with working with code they did not write themselves.

Consider an AI-generated feature that works in a demonstration but exposes customer information under an unusual combination of permissions. Finding that problem requires understanding how the system behaves beyond the assignment. For someone building a career, this changes what counts as evidence of ability: a portfolio becomes more compelling when it explains the decisions, tests and trade-offs behind the finished application, including where AI helped and where its output needed correction.

Those capabilities appear prominently in employer requirements. Draup’s June 2026 analysis of 1.16 million software-development-engineer job descriptions found debugging in approximately 167,000 postings and systems design in 101,000, alongside demand for AI coding tools such as Copilot and Cursor.

Programming, testing and systems knowledge remain essential, and employers typically seek a relevant bachelor’s degree. For people who enjoy building and troubleshooting, internships and projects involving maintenance as well as development provide practical preparation.

BLS reports median annual pay of $135,980 for software developers in May 2025 and projects employment growth of 10% from 2025 to 2035, with demand including AI applications and other software-dependent products and services.

2. Data Scientists

The more organizations expect AI to do, the more they need to understand the data it learns from and the models they rely on. Data scientists bring the statistical and computational expertise to investigate whether the evidence supports the intended use, how reliably a model performs and where its conclusions break down.

AI is changing the work of building those models. Draup identifies modeling and tuning that automated machine-learning tools can perform as more exposed to automation, while highlighting problem framing, statistics and causal rigor as durable data-science capabilities.

Consider a model that accurately predicts which customers will leave. That accuracy does not establish whether offering a discount will persuade them to stay. Answering the second question may require a carefully designed experiment. Scientific judgment has always been central to data science; automating more implementation increases the importance of demonstrating it. A strong project should explain the choice of method, how the model was validated and which conclusions the evidence can support.

This field suits people who enjoy mathematical reasoning and investigating uncertainty. Programming, mathematics and statistics provide the foundation; research projects and internships offer opportunities to work with imperfect data and validate models.

Employers typically seek at least a bachelor’s degree in mathematics, statistics, computer science or a related field; some require or prefer a master’s or doctorate.

BLS reports median annual pay for data scientists of $120,230 in May 2025 and projects employment growth of 35% from 2025 to 2035, supported by expanding uses of data and organizations’ adoption of AI.

3. Cybersecurity Analysts

As businesses embed AI into everyday operations, security expertise becomes essential to determining how much access and autonomy those systems can safely receive. AI also gives attackers additional ways to manipulate systems and expose sensitive information. OWASP identifies manipulation of agent behavior, tool misuse and abuse of permissions as significant risks.

For people developing their careers, this extends established expertise in identity, access controls and incident response into AI-specific threat assessment and testing.

The work suits methodical investigators who want to understand how systems can be compromised. Networking, operating systems and security tools provide the technical foundation.

Employers typically seek a relevant bachelor’s degree and related experience, and may prefer professional certifications. IT support, networking and systems administration can provide routes into the field.

BLS tracks this work under information security analysts: median annual pay was $129,180 in May 2025, with employment projected to grow 21% from 2025 to 2035.

4. Computer Network Architects

As organizations expand their use of AI, the infrastructure underneath it has to keep up. An application that works in a pilot may struggle when thousands of employees use it simultaneously, moving data between offices, cloud services and connected systems. For network architects, this adds new demands to familiar decisions about capacity, security and reliability.

Understanding how the business intends to use AI helps determine where connections could become bottlenecks, what must remain available during a failure and which investments are justified. The role suits people who enjoy tracing dependencies and weighing trade-offs across a whole system.

Preparation combines networking, security and infrastructure design. Employers typically seek a computer-related bachelor’s degree and experience in a related occupation, making architecture a progression from hands-on technical work. Operating and troubleshooting networks, then participating in design and migration projects, provides a practical route into the profession.

BLS reports median annual pay for computer network architects of $134,050 in May 2025 and projects employment growth of 8% from 2025 to 2035, with cloud expansion and infrastructure upgrades supporting technologies such as AI contributing to demand.

5. Database Architects

The more organizations rely on AI to use their information, the more consequential the structure underneath that information becomes. An AI application answering a customer’s question may need to connect a contract, a payment history and a recently negotiated exception. Those records must be organized so the application can retrieve the relevant information while respecting access restrictions. For database architects, this extends established concerns about data design, consistency and security into the requirements of AI-enabled applications.

The work suits people who enjoy organizing complex information and anticipating how design choices affect its use.

Database design, query languages and security are core skills. Employers typically seek a computer-related bachelor’s degree, with experience in database or software-development work providing useful preparation. A practical project can demonstrate access controls, consistency and recovery from failure.

BLS reports median annual pay for database architects of $139,500 in May 2025 and projects employment growth of 9% from 2025 to 2035, linking demand to the data infrastructure needed for AI and other innovation. These figures apply specifically to database architects; BLS projects more limited demand for database administrators as cloud services consolidate parts of that work.

6. Computer Systems Analysts

As organizations introduce AI, someone needs to examine whether the processes they want to automate still make sense. Systems analysts have long connected business requirements with technical solutions. That expertise becomes especially valuable when new capabilities allow organizations to reconsider how work flows, where decisions belong and which steps remain necessary.

An insurance company might use AI to summarize claims faster while leaving the approval bottleneck untouched. Understanding why those approvals exist, what information decision-makers need and how responsibilities could change helps turn a technology investment into an operational improvement.

The role suits people who ask probing questions and can translate between technical and operational colleagues.

Preparation includes process mapping, requirements analysis, technical knowledge and communication. A bachelor’s degree is typical, although some employers accept business or liberal arts graduates with relevant technical skills. Industry experience can help professionals recognize problems and constraints that are difficult to see from a system specification alone.

BLS reports median annual pay for computer systems analysts of $105,850 in May 2025 and projects employment growth of 8% from 2025 to 2035, with expanding IT and AI use supporting demand. To build experience, participate in a process-improvement project and follow it from requirements through implementation and evaluation.

The Tasks AI Is Taking Over From Tech Workers

The tasks that AI is taking over from tech workers, or which is particularly exposed to AI assistance and automation, involves work that is repeatable, clearly specified and easy to check. Those tasks exist inside growing professions too, so demand for a job title can rise while demand for some familiar responsibilities falls. Budgets, strategy and organizational changes also influence hiring.

In a 2026 analysis, Anthropic compared the tasks people perform in different occupations with how people were using its Claude AI for work. Tasks that occupy more of a worker’s time counted more, as did tasks AI performed rather than assisted with. Higher scores indicate more exposure to AI use across a role’s tasks, signaling more potential for the work to change, whether through greater productivity, changing responsibilities or reduced demand.

Computer programmers scored about 75 out of 100 on this measure, compared with 52 for software quality assurance analysts and testers, 47 for computer user support specialists and 29 for software developers.

The question of whether AI will replace entry-level jobs includes a development challenge: many routine assignments have helped beginners learn. Workers need opportunities to investigate errors and understand decisions even when tools produce the first draft. Roles can evolve toward designing, checking and improving the work, provided people get the experience needed to do that well.

For tech workers, these changes raise a practical question: which skills will help them contribute as AI takes on more of the tasks they do today?

The Key Skills That Tech Workers Need To Prioritize

Tech workers need technical depth, AI fluency and the ability to connect their decisions to business consequences. Seek opportunities to investigate failures, defend a decision and receive feedback from someone more experienced.

If you already work in tech, start with a recent project. Where did your experience help you catch a problem, question a requirement or anticipate a consequence? Use those examples to describe your contribution in applications and interviews, then identify what you still need to learn. Pair learning with an assignment where you can apply it. In-demand niche skills can help you specialize, especially when combined with knowledge of a particular industry. The following priorities provide a practical starting point:

• Problem definition: Clarify whose problem you are solving, what improvement matters and what constraints apply before selecting a tool.
• Technical depth: Learn the underlying concepts well enough to investigate unexpected behavior. Familiarity with an interface cannot substitute for understanding a system.
• AI collaboration: Practice specifying work, providing relevant context and deciding which tasks require closer supervision. Explain your process as well as your output.
• Verification: Test for failure, incomplete evidence and misleading results. Record what you checked, what remains uncertain and when you would seek help.
• Systems thinking: Follow the consequences of a change across connected applications, data, users and costs. Small local improvements can create problems elsewhere.
• Business and industry knowledge: Understand how the organization earns money, serves people and manages risk. Use that context to identify worthwhile technical work.
• Communication and influence: Explain alternatives in language decision-makers can use. Surface disagreements and assumptions before they become implementation problems.
• Learning through responsibility: Seek supervised experience with reviews, incidents and difficult decisions. Keep a record of how your reasoning improves, alongside the tools you learn.

Your next career move may have the same title as your last one and demand a different contribution. Look closely at what employers now need you to understand, decide and take responsibility for regarding in-demand tech jobs. Then ask: what in my experience shows I can do that, and what do I need to learn?