Introduction
AI is everywhere—transforming industries, products, and careers. But when most people hear “AI jobs,” they picture data scientists or machine learning engineers. The reality is broader.
From software developers to UX designers, project managers to infrastructure specialists, AI projects depend on many roles working together to succeed. Whether you’re technical or non-technical, experienced in IT or coming from a different professional background, there’s likely a place for your skills in AI.
In this session, we’ll help you:
- Understand how AI teams are structured.
- See where your skills might fit.
- Get familiar with both technical and non-technical roles in AI projects.
Note: Job titles vary across companies. You’ll often need to read between the lines of job descriptions to spot opportunities that match your background.
Why This Matters
AI products aren’t built by algorithms alone. They’re built by people—coordinated across technical, operational, and strategic roles. By understanding the landscape, you can:
- Identify job opportunities beyond typical AI roles.
- Target your CV more effectively.
- Position yourself for a career move into the AI space, even without coding or machine learning experience.
Technical Roles in AI Projects
| Role | Alternative Titles | What They Do |
|---|---|---|
| Software Engineer / Developer | Backend Engineer, API Developer | Build AI-powered software products and APIs. |
| Machine Learning Engineer | ML Engineer, AI Software Engineer | Build and deploy machine learning models into production. |
| Data Scientist | AI Data Analyst | Analyze data, build models for insights or predictions. |
| MLOps Engineer | AI DevOps, ML Pipeline Engineer | Manage ML pipeline, model deployment, automation. |
| AI Research Engineer | AI Research Scientist | Develop experimental AI models, publish research. |
| Data Engineer | ETL Developer | Prepare/manage data for AI models. |
| Cloud Engineer (AI focus) | Cloud Solutions Architect | Build AI infrastructure on cloud platforms. |
| DevOps Engineer (AI pipelines) | AI Platform Engineer | Manage infrastructure supporting AI deployments. |
| Frontend Developer | Web Engineer | Create interfaces for AI-powered products. |
| Backend Developer | API Developer, Server-Side Dev | Build backend services for AI tools. |
| QA Engineer / Test Automation | Quality Automation Specialist | Ensure software quality, test AI functionalities. |
| Security Engineer | AI Security Specialist | Secure AI models, pipelines, and data systems. |
Non-Technical and Coordinating Roles
| Role | Alternative Titles | What They Do |
|---|---|---|
| UX Lead | Lead UX Designer | Define UX vision across AI platforms and tools. |
| Scrum Master | Agile Coach, Delivery Facilitator | Guide teams in agile delivery processes. |
| User Acceptance Lead | UAT Manager | Validate AI solutions meet business/user needs before release. |
| Security Lead | Security Manager | Oversee security controls for AI systems and pipelines. |
| Quality Lead | QA Manager | Lead quality assurance across AI products. |
| Program Lead | Program Manager | Coordinate multiple AI teams to achieve overarching goals. |
| Project Lead (AI Initiatives) | AI Project Manager | Manage and deliver individual AI-focused projects. |
| People Manager (AI Teams) | AI Team Lead, Line Manager | Manage and support professional growth of AI team members. |
| Performance Lead | System Optimization Specialist | Ensure performance and efficiency of AI products and platforms. |
| Internal Architect | Technical Architect | Design internal AI platform architecture and standards. |
| Solution Architect (AI Systems) | Domain Architect, Enterprise Architect | Design end-to-end solution architecture across AI systems and domains. |
| Infrastructure/CI/CD Lead | DevOps Manager | Manage infrastructure and deployment pipelines supporting AI models. |
| Infrastructure Lead (App Layer) | Application Infrastructure Manager | Manage infrastructure supporting AI applications and services. |
| Delivery Lead | Release Manager | Oversee product releases and delivery cadence. |
| Chief Product Owner | Head of Product | Own product vision and strategic alignment across AI teams. |
| Product Owner (AI Teams) | Feature Owner, Agile Product Owner | Prioritize and manage delivery of specific AI product features. |
| Chief Architect | Enterprise Architect | Define and enforce architectural standards across AI platforms. |
| Outbound Product Manager | Go-To-Market PM | Focus on external customers and market needs for AI products. |
| Inbound Product Manager | Technical Product Manager | Define internal feature requirements for AI solutions. |
| Product Manager (AI focus) | Product Owner, AI Solutions Manager | Lead product strategy and development for AI products. |
| Business Analyst | AI Strategy Analyst | Translate business needs into technical AI solutions. |
| Technical Writer | Documentation Specialist | Document AI systems, APIs, and internal processes. |
| Customer Success Lead | Solutions Consultant | Support client onboarding and success with AI solutions. |
| HR / Talent Acquisition Lead | AI Recruiter | Hire and manage talent pipelines for AI teams. |
| Marketing / Sales Specialist | AI Solutions Marketer | Promote and sell AI-powered products and services. |
Key Takeaway
If you’ve worked in:
- Software development,
- Operations or infrastructure,
- Product or project management,
- UX/UI design,
- Marketing, sales, or HR…
…there’s likely a place for your skills within AI projects. The roles may not always mention “AI” directly—but knowing what to look for helps you spot the opportunity.
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Next Blog: Mapping Your Personal Experience
In the next blog, we’ll guide you step-by-step on how to document your current experience using a simple template—so you can evaluate which AI roles you’re best suited for.