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IT Job Roles / AI Engineering & Agents

AI Engineer:
Skills, Certifications & Practice Tests

Discover how AI engineers build, evaluate, secure and deploy intelligent applications and agents. Explore practical skills in Python, generative AI, RAG, model evaluation and cloud platforms, then choose MeasureUp practice tests that match your experience and career goals.

The role at a glance
01
Build AI applications & agentsIntegrate models, APIs, tools and structured workflows into usable software.
02
Connect data and knowledgeUse retrieval, embeddings and data pipelines to ground model responses.
03
Evaluate quality & safetyTest accuracy, relevance, security and behavior before and after release.
04
Deploy and monitor systemsMonitor cost, latency, reliability and access controls in production.
25+ years in certification prep
10M+ tests taken
Practice + Certification Modes
Answer explanations & reporting
Role explained

What does an AI Engineer do?

An AI engineer designs, builds and operates software powered by artificial intelligence. Depending on the team, this may involve integrating foundation models, creating retrieval-augmented generation (RAG) applications, building agents, evaluating outputs and monitoring deployed services. The role combines software engineering, data skills, cloud operations and responsible AI practices.

01

Python, APIs & software engineering

Build maintainable Python services, connect model APIs, write tests, version changes and collaborate through code review.

02

Model integration & orchestration

Select suitable models, design tool calls and agent workflows, and coordinate multi-step application behavior.

03

RAG, retrieval & data pipelines

Prepare knowledge sources, embed and index content, and combine search results with prompts to ground answers.

04

Evaluation, testing & observability

Track answer quality, factuality, task success, latency and regressions with test datasets, traces and feedback.

05

AI security, privacy & governance

Mitigate prompt injection, data leakage and unsafe tool use with permissions, safeguards and human oversight.

06

Deployment, performance & cost

Manage cloud deployment, access, reliability, version changes and budget across the AI application lifecycle.

AI Engineer vs. Software Developer

AI engineers build model-powered applications and agents. Software developers build a broader range of applications and services; the roles overlap when AI becomes part of a product. Explore Software Developer →

AI Engineer vs. Data Engineer

AI engineers focus on integrating and operating intelligent features. Data engineers primarily build reliable data ingestion and transformation systems that AI applications may depend on. Explore Data Engineer →

Your AI engineering career path

How to become an AI Engineer

There is no single required certification. Start with programming and AI fundamentals, build a working application using models and real data, learn to evaluate and secure it, then prepare for the certification that best fits your technology stack.

01

Learn Python, APIs & AI basics

Practice Python, Git, REST APIs, data preparation and the differences between classical ML, generative AI and LLMs.

Demonstrate a small, tested AI API or application
02

Build a grounded AI application

Create an app with a model API, retrieval from a trusted document set, and clearly defined prompts and evaluation cases.

Add RAG, citations and repeatable evaluation examples
03

Secure, evaluate & operate it

Test for inaccurate responses, prompt injection and permission failures; add tracing, monitoring, cost and latency checks.

Document risks, safeguards and operational trade-offs
04

Show your work & validate skills

Publish documented projects and use a targeted exam such as AI-103 for Azure agents, or foundation tests if you are starting out.

Use projects plus practice tests to show readiness
Choose preparation by your AI focus

AI Engineer practice tests and related certifications

Start with the AI engineering credential closest to your technical work. Foundation, architecture, developer-tool and data-platform practice tests can complement that path, but they do not all certify the same role or skill level.

Core AI engineering AI-103

Azure AI Apps & Agents Developer

Develop and operate generative AI applications, agents, retrieval solutions and multimodal workflows in Microsoft Foundry.

View practice test
Advanced agent architecture AB-100

Agentic AI Business Solutions Architect

Design secure and scalable AI-driven business solutions. This advanced Microsoft credential has additional certification requirements.

View practice test
AI fundamentals / AWS AIF-C01

AWS Certified AI Practitioner

Build foundational knowledge of AI, generative AI, foundation models, responsible use and AWS services. Not a hands-on engineering exam.

View practice test
AI-assisted development GH-300

GitHub Copilot

Practice responsible AI-assisted coding, prompt context, Copilot features, testing and privacy settings. Complementary developer skills.

View practice test
GenAI foundations FOUNDATIONS

Generative AI Foundations

Review generative AI concepts, model families, use cases and limitations before moving to specialized engineering objectives.

View practice test
Related data engineering DP-700

Microsoft Fabric Data Engineer

Strengthen ingestion, data transformation and platform operations that support AI data workflows. This is a data engineering credential.

View practice test
Select the right level. AI-103 is the clearest direct engineering path here. AB-100 is an advanced solution architecture track, with an associate credential requirement in addition to the exam. AWS AIF-C01 and Generative AI Foundations cover foundational understanding; GH-300 covers AI-assisted software development; DP-700 covers supporting data infrastructure. The AB-100 skills measured changed on October 14, 2026; compare the current Microsoft study guide with MeasureUp coverage before purchasing. Retired AI-900 and AI-102 exams are not recommended as current certification routes. See the official AI-103 study guide.
Why MeasureUp

Practice AI concepts. Identify what needs more work.

Hands-on AI projects demonstrate engineering ability; certification practice tests add an organized way to check exam objectives, understand mistakes and measure progress before your next attempt.

01

Realistic exam simulation

Use timed Certification Mode to test your understanding of the objectives for your chosen certification.

02

Targeted AI topic practice

Configure Practice Mode to revisit model selection, agents, RAG, AI safety or related knowledge areas when included in the exam.

03

Detailed explanations & references

Review the reasoning behind correct and incorrect answers, with learning references for further investigation.

04

Objective-level readiness reports

Compare performance across objectives and repeated attempts, then target weaker domains instead of revising everything equally.

One platform. Two ways to prepare.

Building AI skills yourself or developing an AI team?

Validate AI engineering knowledge individually or support structured certification preparation across development, data, cloud and agent-building teams.

For individuals

Build practical AI skills alongside exam preparation.

Combine a real Python or agent project with AI-103 preparation, or start with foundational concepts before advancing to architecture and deployment.

Practice and Certification Modes
Detailed explanations and references
Track progress across selected AI exams
Explore individual plans
For AI & engineering teams

Support AI training with consistent progress visibility.

Give learners structured access to relevant practice tests, review objective-level results and support teams working on cloud AI and generative applications.

Team access and license management
Scores and objective-level reporting
Flexible solutions for different team sizes
Explore business solutions
Quick answer

What does an AI Engineer do?

An AI engineer develops and maintains applications that use artificial intelligence, including machine learning services, generative AI models and autonomous or supervised agents. The work involves building APIs and workflows, connecting trusted data, evaluating results, deploying services and applying privacy, security and operational controls.

A typical AI engineering project combines software development, data retrieval, model integration, testing, monitoring and human oversight. AI engineers collaborate with data engineers, software developers, cloud specialists and solution architects; the precise role varies by employer and platform.

AI Engineer career FAQs

Common questions about AI engineering careers

Straight answers about AI engineering responsibilities, agent development, certification options, hands-on skills and safe deployment.

What does an AI Engineer do?
An AI engineer designs, integrates, tests and operates AI-powered applications. Typical responsibilities include working with models and APIs, building agents or retrieval systems, evaluating outputs, managing cloud services and monitoring security, quality, cost and reliability.
How can I become an AI Engineer?
Start with Python, software engineering, APIs and data basics. Build a small model-powered application, add retrieval and evaluations, then secure and deploy it. Use a certification that matches your platform, such as AI-103 for Azure AI applications and agents. A portfolio and practical experience remain important.
What skills should I learn for AI engineering?
Python, APIs, Git, SQL or data preparation, foundation-model integration, retrieval-augmented generation (RAG), agent tools, evaluation methods, cloud deployment, monitoring and responsible AI. Deep model-training skills may matter more for machine learning engineering roles.
What is the difference between an AI Engineer and a Data Scientist?
AI engineers usually focus on integrating and operating AI capabilities within production applications. Data scientists often focus on exploring data, developing statistical or predictive models and testing hypotheses. In smaller teams, the responsibilities can overlap.
What are RAG and AI agents?
Retrieval-augmented generation (RAG) combines search over trusted information with a generative model to ground responses. AI agents use models to plan or perform steps with tools and workflows. An agent may use RAG, but RAG alone is not necessarily an agent.
Is Microsoft AI-103 a good certification for AI Engineers?
AI-103 is designed for engineers and developers who build and deploy AI applications and agents on Azure. It covers Microsoft Foundry, generative AI, agentic workflows, computer vision, text analysis and information extraction. Relevant Python and Azure experience is useful.
Should I prepare for AI-900 or AI-102?
AI-900 and AI-102 are listed by MeasureUp as retired exams. For a current Azure AI apps and agents developer path, review AI-103 and confirm the latest requirements on Microsoft Learn before purchasing practice material.
Is AWS AI Practitioner or Microsoft AB-100 suitable for beginners?
AWS AI Practitioner AIF-C01 validates foundational concepts and does not test building or deploying AI systems. AB-100 is an advanced agentic AI business solution architecture exam; the Expert credential also requires an eligible associate certification. They serve very different audiences.
How can companies measure AI certification readiness?
MeasureUp offers business solutions for practice-test access, administration and learner reporting. Managers can use exam objective reports to identify learning gaps, while still evaluating real project outcomes, responsible AI practices and operational skills outside the test platform.
Engineering intelligent applications

Ready to build your AI Engineer career?

Choose an AI practice test aligned with your experience, learn from your results and keep building secure, evaluated applications that demonstrate your skills.

Career and certification references

Professional skills: Microsoft AI-103 study guide (AI apps, agents, evaluation and deployment), NIST AI Risk Management Framework and OWASP guidance for LLM application security. Responsibilities and tool stacks vary by employer.

Certification references: Microsoft AI-103, Microsoft AB-100 credential requirements and AWS AI Practitioner exam guide. Confirm current exams, skills measured, prerequisites and updates with each certifying vendor.

MeasureUp resources: Practice Test modes, individual subscriptions, Seats for teams and business solutions. Practice tests are independent exam-preparation tools and do not contain live exam questions or replace hands-on development and AI safety evaluation.