Radio engineering
ISCO-08 2153, 3522Radio engineer, RAN optimisation engineer, Radio planning engineer, Radio network performance engineer, 5G radio expert
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Defining policies for self-optimising networks
- Validating parameter changes proposed by algorithms
- Propagation physics and spectrum strategy
- Python and data analysis on network counters
Losing value
- Manual tuning of tilts and neighbour lists
- Analysing drive tests by hand
- Standard KPI reporting
- Repetitive coverage prediction runs
How the job will change
Radio optimisation is already heavily automated: self-optimising functions adjust tilts, power and neighbour relations, and machine learning models detect degraded cells from counters before customers complain. Drive tests are partly replaced by trace and crowdsourced data analysed automatically. Parameter work that used to take an engineer days becomes a set of proposals to review and approve.
The radio engineer moves from tuning cells to setting the rules the algorithms follow, and checking them. Knowledge of propagation and spectrum stays essential to spot a wrong recommendation, in dense areas or during large events. A good radio engineer tomorrow pairs that physical understanding with real ease with data, and can explain what an energy-saving algorithm actually does to coverage.
- 2026-2027
- ML models detect degraded cells; engineers approve proposed parameter changes.
- 2028-2030
- Closed-loop optimisation and AI energy saving become the norm on most sites.
- 2031+
- Fewer optimisers, more experts defining policies and auditing algorithm behaviour.
Radio expertise takes years to build and many experienced engineers are nearing retirement. If juniors only supervise automated tools, nobody will understand the physics well enough to catch algorithm errors. See the seniority outlook below.
What if you hired fewer juniors?
Your 2036 seniors are the juniors you hire today.
Advanced settings modified
2026 2036
The AI Cookbook 2026
albert's guide to cut through the noise around generative AI and turn it into workforce decisions.
- What AI actually changes in jobs and skills
- Why the junior pipeline matters more than most companies realise
- How to integrate AI into workforce planning

Run these scenarios on your actual workforce
AI Impact Diagnostic: 6 to 8 weeks, your data in albert, three costed scenarios and the projected seniority mix for each job family.
How the numbers are built
Every headcount figure combines four assumptions. Three come pre-filled from public research and job family defaults. The strategic ceiling is yours to set.
Seniority outlook
A flow model with three levels of experience in the profession. Promotion and exit rates are set so that today's mix stays stable when hiring does not change: any gap you see comes from the junior hiring cut alone.
Sources
- International Labour OrganizationGenerative AI and jobs: a refined global index of occupational exposure (2025)
- ILO datasetTask-level GenAI exposure scores by ISCO-08 occupation
- AnthropicAnthropic Economic Index (June 2026 release, April and May 2026 usage data)
- Stanford Digital Economy LabCanaries in the Coal Mine? Six facts about the recent employment effects of AI