Every technology hiring conversation we have eventually arrives at the same place: AI and data roles. Demand has outpaced supply for years, and 2026 has only sharpened the imbalance as enterprises move from experimentation to production AI systems. What's changed is the market's sophistication — both employers and candidates have learned hard lessons, and the gap between realistic and wishful hiring plans has never mattered more.
Here's the picture from our Technology desk's placement data.
The Title Means Less Than the Stack
"AI engineer" now spans everything from prompt-pipeline integration work to distributed training infrastructure to applied research. Compensation across that span varies by a factor of three or more. Before benchmarking or sourcing, define the actual work: production ML systems, data platform engineering, LLM application development, and research-adjacent roles are four different markets with four different talent pools, timelines, and price points.
Realistic Compensation Bands, Honestly Stated
For genuinely production-experienced profiles — engineers who have shipped and operated ML or LLM systems at scale, not completed coursework about them — premiums of 30-50% over equivalent-seniority software engineering roles remain standard. Attempting to hire these profiles inside standard engineering bands doesn't produce cheaper hires; it produces six-month-old open requisitions. If the budget genuinely can't stretch, the honest alternative is hiring strong engineers and building the specialisation internally — a slower but compounding strategy.
Realistic Timelines
- Data engineers (strong, production-experienced): 6-10 weeks with focused sourcing
- ML engineers with deployed-systems experience: 10-16 weeks; shorter only with compensation clearly above market
- Senior/staff-level AI platform leads: 4-6 months; these are retained-search profiles, not job-posting profiles
- Research-adjacent specialists: highly variable; network-driven sourcing matters more than any timeline assumption
What Actually Attracts These Candidates
Three factors consistently outweigh marginal compensation differences in our offer-acceptance data: the quality and scale of the data or systems they'll work with, the technical credibility of the people they'll report to, and evidence that the organisation ships — that models reach production rather than dying in proof-of-concept purgatory. Employers who can demonstrate all three convert candidates at significantly higher rates than the market's top payers who can't.
The Screening Problem — and Solving It
The AI talent market has a noise problem: course certificates and keyword-rich résumés vastly outnumber production experience. Effective screening focuses on shipped systems — what did you build, what broke, what would you do differently — rather than tool checklists. This is where a specialised recruitment partner earns its fee: our Technology desk pre-screens for production depth before candidates ever reach your interview panel.