AI demand now spans product engineering, data, infrastructure, security, operations, and customer-facing work. The useful question is no longer whether a role mentions AI, but whether AI is the product, a required delivery capability, or supporting context.
96 live roles7 hiring companies16% remote-friendly
Vacancy evidence map
Where AI carries hiring weight
Each segment represents roles where AI was parsed at that evidence tier. This keeps a required production skill separate from contextual stack language.
8 Core74 Required6 Supporting8 Optional
82core + required85% of mentions
13.7roles per company7 employers
Market interpretation
What AI demand means in practice
ApplyDjinn separates explicit AI requirements from broad mentions. Core and required evidence points to roles where candidates must design, ship, evaluate, or operate AI systems; supporting evidence often describes a product environment where adjacent engineering depth still matters more than model expertise.
Strong applications connect AI vocabulary to shipped outcomes: evaluation quality, retrieval accuracy, latency, cost, safety, workflow adoption, or measurable automation. A generic interest in AI is weaker evidence than one small system with a documented decision trail.
AI / ML engineeringApplied product engineeringData and platformAI-enabled operations
Reading the market
Signals to look for
LLM, RAG, Python, data pipelines, and cloud services frequently form the implementation layer.
Evaluation, observability, security, and human review distinguish production work from demos.
AI appears in both specialist roles and conventional software jobs with an AI-assisted product surface.
Do all AI vacancies require machine-learning research experience?
No. Many roles need software, data, cloud, product, or operations experience around an AI-enabled workflow. The demand-tier chart shows whether AI is central or contextual.
What is the strongest portfolio evidence for an AI role?
A small deployed system with evaluation results, cost and latency constraints, failure analysis, and a clear explanation of what the candidate personally built.
How AI appears in job requirements
Inferred from the title and requirement wording in 96 active roles; each role is counted once. Read the methodology.
Posting evidence, not a company-wide stack claim
Main / core
8%(8)
Required
77%(74)
Supporting / context
6%(6)
Nice-to-have
8%(8)
Skills mentioned with AI
A shared posting does not always require both skills. Explicit alternatives are counted separately.