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AI Engineer Jobs

AI engineers build AI-powered products and features - integrating models into applications, building agents and LLM-backed workflows, and shipping the layer between a trained model and the end user. The role overlaps with software engineering but centers on model APIs, prompting, and AI-specific infrastructure.

527 live AI Engineer roles across the AI employers we track - updated hourly, apply directly.

4new AI Engineer roles posted this week

AI Engineer roles are rare: 527 live right now. Get the new ones every Monday.

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Latest AI Engineer roles

Engineering around a probabilistic component

AI engineer is the newest title in this list, and it exists for a specific reason: capable models became available through an API, so building a useful AI product stopped requiring the ability to train one. That decoupling created a role for engineers who are excellent at systems and product work and who treat the model as a component they call rather than as an artifact they own.

Most postings do not ask you to train models. They ask for retrieval systems that put the right context in front of a model, agent loops that call tools and recover when a tool fails, prompt and context management, caching, streaming, cost and latency budgets, and evaluation harnesses that catch regressions before users do. Fine-tuning appears occasionally, and usually as an optimization applied late rather than as the core of the job.

The core competence is systems engineering around a probabilistic component. Everything a backend engineer already knows about timeouts, retries, idempotency, queueing, and graceful degradation still applies, but the component in the middle returns a different answer to the same input, fails in ways a type system cannot catch, and can be steered by text it reads from an untrusted source. Strong software engineers convert into this role faster than researchers do.

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Frequently asked questions

How many AI Engineer vacancies are live on the board?
We are tracking 527 live AI Engineer roles across the AI companies we monitor, updated hourly. Each listing links straight to the employer's own application page.
Do AI engineer roles require training or fine-tuning models?
Usually not. The common expectation is building on models reached through an API or a hosted endpoint: retrieval, agents and tool use, prompt and context management, latency and cost control, and evaluation. Fine-tuning appears in a minority of postings, and typically as a later optimization rather than as the central responsibility.
How do you test software built on a non-deterministic model?
With evaluation suites rather than assertions alone. Teams keep a versioned set of representative inputs with graded expectations, score every change against it before shipping, and add each production failure to the set. Deterministic tests still cover the surrounding system, including parsing, tool contracts, retries, and fallbacks, which is where many real defects live.
Can a backend engineer move into AI engineering?
It is the most common route in. Distributed systems, API design, queueing, observability, and cost control transfer directly, and the new material is learnable on the job: retrieval, agent design, prompt and context handling, evaluation, and the security implications of untrusted text reaching a model. Model training theory is rarely the blocker.

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