AI system design
Connects user need, model limits, data, evaluation, guardrails, fallback, and operations.
Starts with a model or agent pattern before defining the task and risk.
Software, Product, Data & AI
AI Engineer Job Description
An AI Engineer builds reliable product capabilities around machine-learning or foundation models, combining software delivery with evaluation, safety, data, and operational controls.
Define the use case, model approach, data access, evaluation, human oversight, and production ownership before publishing.
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Replace bracketed details, remove anything that is not genuinely required, and obtain the appropriate internal approval before advertising.
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AI Engineer
- Location
- [Location or working arrangement]
- Employment type
- [Full-time or contract]
- Reports to
- [AI Engineering Manager, Head of AI, or Engineering Director]
About the role
[Company name] is seeking an AI Engineer for [use case]. You will design and build model-powered product workflows, establish evaluation and safety controls, integrate approved data and tools, monitor behaviour, and improve the system from real evidence.
What you will be responsible for
- Build model, retrieval, tool-use, prompt, and application components within secure product architecture.
- Create representative evaluations covering quality, safety, latency, cost, and failure conditions.
- Implement data access, privacy, permission, guardrail, fallback, and human-review controls.
- Version prompts, models, datasets, configurations, and decisions for traceable change.
- Monitor production behaviour and coordinate incidents, feedback, and model or product improvement.
What success looks like
- AI behaviour meets defined task and safety thresholds on representative cases.
- Users understand limitations, review responsibilities, and safe recovery routes.
- Model or prompt changes are evaluated, approved, monitored, and reversible.
Essential qualifications
- Strong software engineering with practical model or AI application delivery.
- Experience with evaluation, retrieval, APIs, data pipelines, testing, and observability.
- Ability to reason about privacy, security, bias, misuse, hallucination, and human oversight.
- Clear documentation and collaboration across technical, product, domain, and governance teams.
Preferred qualifications
- Experience with [model family, modality, agent workflow, domain, or deployment environment].
- Exposure to ML platforms, red teaming, model governance, fine-tuning, or high-scale inference.
Tools and working knowledge
- Model APIs, SDKs, orchestration, and evaluation frameworks
- Vector, search, data, experiment, and observability platforms
- Version control, CI/CD, cloud, security, and incident systems
Compensation: [Add approved range, currency, bonus or equity, benefits, compute access, on-call terms, and location basis.]
[Company name] will discuss reasonable adjustments and provide accessible technical-assessment options.
How to apply
Apply through [method] with a production AI example covering your contribution, evaluation, controls, deployment, and learning.
Performance expectations
What good performance looks like
Use these outcomes to replace vague activity lists with the evidence the hiring manager expects to see after the person joins.
AI releases have explicit task, benchmark, safety, cost, and fallback criteria.
Production failures produce new evaluation cases and system improvements.
The product uses the simplest suitable model and architecture for the user need.
Hiring-manager intake
Questions to settle before advertising
Record specific answers so sourcing, screening, and interview decisions use the same definition of the role.
- 1
Which user decision or task will AI support, and what must remain human-controlled?
- 2
Which models, data, tools, deployment environments, and governance requirements apply?
- 3
Who owns evaluation, security, legal review, product decisions, and production incidents?
- 4
What quality, safety, latency, cost, and adoption evidence defines success?
Evaluation criteria
Evidence to use in a AI Engineer scorecard
Agree the criteria before interviews begin, then score examples against the same evidence standard.
Evaluation judgement
Builds representative cases, baselines, error analysis, thresholds, and regression checks.
Relies on a few impressive examples or one aggregate score.
Responsible delivery
Designs proportionate privacy, permission, safety, disclosure, and human-review controls.
Treats policy wording as sufficient protection from technical failure or misuse.
Structured interview
AI Engineer interview questions and strong signals
Ask the same core questions in the same order, then use follow-ups to understand the candidate’s individual contribution.
1
Tell me about an AI prototype you decided was not ready to release.
Strong answer signal
Shows specific evaluation gaps, risk, communication, alternative action, and readiness criteria.
2
How would you detect a retrieval system giving plausible answers from irrelevant evidence?
Strong answer signal
Covers corpus, retrieval metrics, groundedness, citations, adversarial cases, user feedback, and monitoring.
3
When would you choose rules or search over a generative model?
Strong answer signal
Balances determinism, risk, cost, latency, maintainability, data, and user value.
Related titles
Check the scope behind the title
Generative AI Engineer — focuses on foundation-model applications and evaluation.
Applied AI Engineer — combines model adaptation with product engineering.
Machine Learning Engineer — usually has deeper ownership of training, serving, and ML infrastructure.
Common hiring mistakes
Problems to remove before publishing
Using “AI” without naming the user task, evaluation, data, or failure owner.
Expecting research, ML platform, product engineering, data engineering, and governance from one person.
Assessing only prompt tricks instead of software, evaluation, safety, and operations.
Continue the hiring workflow
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Source and review notes
How this template was prepared
- Language
- International English
- Prepared by
- ATZ CRM Editorial Team
- Review
- ATZ CRM Recruitment Editorial Review
- Last reviewed
- 2026-08-05
O*NET: Software Developers
Reference for occupation tasks, knowledge, skills, abilities, and work activities.
O*NET: Data Scientists
Reference for occupation tasks, knowledge, skills, abilities, and work activities.
ESCO: occupations and skills
Reference for internationally recognised occupation and skills terminology.
Qualifications, compensation, licences, working conditions, and equal-opportunity wording must be checked for the role and location before use.
Recruiter questions
AI Engineer job description FAQs
What should an AI Engineer job description include?
State the use case, models, data, architecture, evaluation, guardrails, human oversight, deployment, monitoring, and production ownership.
How is an AI Engineer different from an ML Engineer?
AI Engineers often integrate existing models into products. ML Engineers commonly own deeper training, serving, and ML-platform systems, though roles overlap.
Should prompt engineering be the main requirement?
Usually not. Production roles also need software, data, evaluation, safety, security, monitoring, and product judgement.