AI CAREERS

9 June 2025

 AI Careers

(Salary ranges, career paths - to be added)

1.    Job Title: AI Prompt Engineer

Overview:
As an AI Prompt Engineer, you will design, test, and refine prompts to improve the performance of AI language models. This role sits at the intersection of software engineering, linguistics, and user experience.

Key Responsibilities:

• Craft and iterate on high-quality prompts for AI models.
• Analyse model outputs to refine instructions.
• Collaborate with developers and product teams to implement prompt-based solutions.

Required Skills:

• Strong communication and analytical thinking.
• Familiarity with LLM behaviour (e.g., GPT-4, Claude).
• Basic programming knowledge (e.g., Python, APIs).

 

2.    Job Title: AI Developer Advocate

Overview:
The AI Developer Advocate bridges the gap between AI tool creators and the developer community. You’ll help others build smarter tools with AI by writing content, hosting tutorials, and gathering feedback.

Key Responsibilities:

• Educate developers on how to integrate AI tools.
• Create demos, sample code, and blog posts.
• Represent the company at meetups and conferences.

Required Skills:

• Strong public speaking and writing skills.
• Deep understanding of developer workflows.
• Experience with modern AI libraries and APIs.

 

3.    Job Title: AI Integration Engineer

Overview:
This role focuses on embedding AI tools into existing business systems. As an AI Integration Engineer, you’ll ensure smooth, secure, and scalable implementation of AI functionalities in real-world applications.

Key Responsibilities:

• Integrate APIs from AI providers into enterprise apps.
• Monitor performance and error handling.
• Collaborate with cross-functional teams to improve system architecture.

Required Skills:

• Backend software development (Python, Java, Node.js).
• Understanding of API protocols and data handling.
• Cloud deployment and version control systems.

 

4.    Job Title: AI Solutions Designer

Overview:
As an AI Solutions Designer, you’ll map business problems to AI use cases and design human-in-the-loop workflows. You combine technical acumen with strategic thinking.

Key Responsibilities:

• Work with clients to scope AI projects.
• Design system architecture and user journeys.
• Define evaluation metrics and testing scenarios.

Required Skills:

• UX/UI awareness.
• Strategic consulting or product design experience.
• High-level understanding of AI capabilities.

 

5.    Job Title: AI Test & QA Analyst

Overview:
This role ensures AI tools work reliably and ethically. You'll test systems for accuracy, fairness, and performance across different scenarios.

Key Responsibilities:

• Test prompt reliability and LLM outputs.
• Conduct edge-case scenario analysis.
• Build test suites and simulate user inputs.

Required Skills:

• QA methodologies.
• Familiarity with prompt tuning and LLM limits.
• Documentation and reporting skills.

 

6.    Job Title: AI Technical Writer

Overview:
You translate complex AI systems into clear, useful documentation. You’ll work alongside developers and product teams to produce user guides, API docs, and onboarding material.

Key Responsibilities:

• Write and maintain technical documentation.
• Organise help centres and chatbot documentation.
• Ensure clarity, consistency, and accuracy.

Required Skills:

• Strong writing and editing skills.
• Technical background or ability to grasp complex systems.
• Familiarity with markdown, API tooling, and diagrams.

 

Update

Updated Job Descriptions for Emerging AI Roles in 2025

(Post-Prompt Engineering Era - prompt engineering was a stepping Stone in to AI. A couple of years ago, but now anyone who can type is expected to be able to write prompts, it's been operationalized, has become just a part of our daily lives, whether you are an office worker or an individual)

7. Job Title: AI Trainer

Overview:

An AI Trainer develops, tests and refines AI behaviour, ensuring natural, useful, and contextually appropriate responses in conversation-based systems. You’ll play a critical role in improving chatbot understanding, tone, and alignment with user intent.

Key Responsibilities:

Design realistic and varied user interaction scenarios.

Fine-tune chatbot behaviour based on user feedback and performance logs.

Create annotated datasets to improve model alignment.

Collaborate with data scientists and NLP engineers to deploy updated models.

Required Skills:

Linguistic awareness and UX sensitivity.

Familiarity with AI training pipelines and prompt tuning.

Ability to detect bias, hallucinations, or misalignment in model outputs.

Ideal Background:

Former content creators, UX writers, linguists, or prompt engineers transitioning to model behaviour design.

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8. Job Title: AI Data Specialist

Overview:

The AI Data Specialist ensures AI models are trained on clean, relevant, and well-structured data. You will be responsible for preparing datasets, enforcing data governance, and continuously auditing data pipelines for quality.

Key Responsibilities:

Clean and structure large datasets for model consumption.

Detect anomalies, duplicates, or corrupted entries in training sets.

Collaborate with AI trainers and engineers to ensure data quality and relevance.

Maintain documentation and lineage tracking for data assets.

Required Skills:

Experience with SQL, Python (Pandas), and data labelling tools.

Understanding of machine learning model data needs.

Strong attention to detail, with an eye for statistical anomalies.

Ideal Background:

Data analysts or engineers pivoting into AI infrastructure support.

9. Job Title: AI Security Specialist

Overview:

As an AI Security Specialist, you’ll safeguard AI systems from evolving threats such as prompt injection, data poisoning, and adversarial attacks. You will work at the cutting edge of cybersecurity and machine learning.

Key Responsibilities:

Conduct vulnerability assessments on AI systems.

Design defences against prompt manipulation and misuse.

Ensure safe deployment of models within enterprise environments.

Monitor for suspicious access, misuse patterns, and insider threats.

Required Skills:

Strong grounding in cybersecurity principles and threat modelling.

Familiarity with LLM architecture, sandboxing, and red teaming techniques.

Knowledge of privacy-preserving AI techniques (e.g. differential privacy).

Ideal Background:

Cybersecurity professionals with interest in emerging AI threats; or ML engineers upskilling in security.

 

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