Not military jargon: “Forward Deployed,” Applied” and other AI job terms explained

The artificial intelligence job market has exploded, but the terminology remains bewildering. Even seasoned tech professionals struggle to decode whether an “Applied AI Engineer” differs meaningfully from an “AI Forward Deployed Engineer”—and for hiring managers outside the tech sphere, these distinctions can feel completely opaque.

This confusion stems from AI’s rapid evolution. New roles emerge overnight, established titles shift meaning between companies, and the underlying technology advances faster than human resources departments can standardize their job descriptions. The result is a professional landscape where one title might describe three entirely different roles across three different organizations.

Here’s a practical decoder for the most common AI job titles, organized around the patterns that actually drive how these roles are named and structured.

The AI job title formula

Nearly every AI job title follows a predictable pattern, mixing and matching from three categories: modifiers, domains, and core roles. Understanding this formula helps decode even the most creative variations.

Modifiers describe how the work gets applied:

• Forward Deployed: Customer-facing roles that implement AI solutions directly with clients
• Applied: Product-focused positions that use existing AI models to solve business problems

Domains indicate the type of AI technology:

• AI: Catch-all term covering the entire field
• ML (Machine Learning): Traditional model training for specific tasks like recommendations or fraud detection
• Gen AI (Generative AI): Text, image, and content creation systems

Core roles define the actual job function:

• Engineer: Builds and implements systems
• Researcher/Scientist: Develops new techniques and runs experiments
• Solution Architect: Designs technical approaches for complex implementations

This framework generates combinations like “Applied AI Engineer” or “Forward Deployed ML Solution Architect,” with each component adding specific meaning about responsibilities and focus areas.

Understanding the modifiers

Forward Deployed professionals work directly with customers to implement AI-powered solutions. They function as technical consultants who learn clients’ specific business constraints and translate requirements into working applications. These roles require both deep technical skills and the ability to rapidly acquire domain expertise in industries ranging from healthcare to logistics.

The venture capital firm Andreessen Horowitz describes these professionals as essential for enterprise AI adoption, comparing the dynamic to “your grandma getting an iPhone: they want to use it, but they need you to set it up.” Forward Deployed Engineers typically spend significant time on-site with clients, building custom integrations and providing ongoing technical support.

Applied roles focus on creating products and features powered by existing AI models rather than developing the underlying technology itself. Applied AI professionals work with pre-trained systems like large language models—the technology behind ChatGPT—to solve specific business problems. They might build a customer service chatbot, create automated document processing systems, or develop recommendation engines for e-commerce platforms.

The key distinction is that Applied professionals use AI as a tool rather than advancing the science itself. They’re product builders who happen to work with artificial intelligence, similar to how mobile app developers use smartphones as their platform without necessarily designing the phone’s operating system.

Decoding the domains

AI serves as the broadest category, encompassing everything from traditional data analysis to cutting-edge language models. When you see “AI” in a job title without additional qualifiers, expect a role that could involve any type of artificial intelligence work.

ML (Machine Learning) typically indicates work with traditional predictive models designed for specific, narrow tasks. ML professionals might build systems that detect fraudulent transactions, recommend products to shoppers, or predict equipment failures in manufacturing. These models usually operate behind the scenes as components within larger applications, and their outputs feed into other business processes rather than being consumed directly by end users.

Gen AI (Generative AI) emerged as a distinct category after ChatGPT’s launch in late 2022, referring to systems that create new content rather than just analyzing existing data. Gen AI roles typically involve building applications where users directly interact with AI-generated text, images, audio, or video. However, this distinction is becoming less meaningful as large language models increasingly handle non-generative tasks like data extraction and analysis.

Core role definitions

Engineers in AI contexts build, deploy, and maintain the systems that put artificial intelligence to work. However, the specific responsibilities vary dramatically depending on the modifiers and domain. An Applied AI Engineer might spend their time integrating ChatGPT’s API into a customer support platform, while an AI Engineer at a research lab might optimize the infrastructure that trains new models.

Researchers and Scientists (terms used largely interchangeably in industry) develop new AI techniques and run experiments to advance the field’s capabilities. However, the academic connotations of “researcher” create confusion in corporate environments. Some industry “researchers” work on purely experimental projects where failed hypotheses are valuable learning experiences. Others have product-focused objectives and business metrics tied to their performance, making them researchers in title only.

This tension has intensified as AI companies mature from experimental labs into product-focused businesses. OpenAI, the company behind ChatGPT, has maintained similar “Research Scientist” job postings seeking candidates who can “discover simple, generalizable ideas that work well even at large scale.” Yet the company’s evolution toward commercial products has blurred the line between pure research and product development.

Solution Architects design technical approaches for complex AI implementations, typically working with enterprise clients who need sophisticated, custom solutions. They bridge the gap between business requirements and technical possibilities, creating detailed plans for how AI systems will integrate with existing infrastructure and workflows.

Real-world examples

AI Researcher positions focus on advancing the fundamental science behind artificial intelligence. These professionals form hypotheses about how AI systems could work better, design experiments to test their theories, and often publish their findings to contribute to the broader field’s knowledge base. Meta’s aggressive hiring of AI researchers has brought significant attention to these roles, as the company seeks to maintain its competitive edge in the rapidly evolving AI landscape.

Applied AI Engineers represent the most common category in today’s job market. Google DeepMind, the search giant’s AI research division, describes these roles as focused on “rapidly developing new features and working across partner teams to deliver solutions.” The emphasis is on translating existing AI capabilities into practical applications rather than developing new AI techniques.

Applied AI Solution Architects help enterprise customers design and plan AI implementations. Anthropic, the company behind the Claude AI assistant, seeks professionals who can “become trusted technical advisors helping large enterprises understand the value of Claude and paint the vision on how they can successfully integrate and deploy Claude into their technology stack.” These roles combine deep technical knowledge with customer-facing skills.

AI Forward Deployed Engineers work directly at client sites to implement AI solutions. Salesforce, the customer relationship management software company, describes these roles as requiring professionals who can “deeply understand customers’ most complex problems, architect sophisticated solutions, and lead the end-to-end technical delivery of innovative, impactful solutions.” The emphasis on rapid domain expertise acquisition makes these among the most challenging AI roles.

The evolving landscape

The “AI Engineer” title without modifiers remains the most ambiguous, covering everything from applied work to foundational model development. The influential AI newsletter Latent Space predicted that AI Engineering would emerge as a distinct discipline, similar to how “site reliability engineer” and “data engineer” became established specializations.

However, the market has moved toward more specific titles using the “Applied” modifier for clarity. Major AI labs like OpenAI and Anthropic rarely use generic “AI Engineer” titles on their career pages, preferring domain-specific engineering roles focused on areas like performance optimization, infrastructure, or model inference—the process of getting trained AI models to generate responses.

Practical considerations for hiring and career planning

When evaluating AI roles, focus on the actual responsibilities rather than getting caught up in title variations. A “Machine Learning Engineer” at a startup might have broader responsibilities than an “Applied AI Engineer” at a large corporation, depending on team size and organizational structure.

For hiring managers, consider that candidates with traditional software engineering backgrounds can often transition effectively into Applied AI roles with appropriate training, while Researcher positions typically require advanced degrees and demonstrated experience with experimental methodology.

The field’s rapid evolution means that today’s specialized roles might become tomorrow’s baseline requirements. Understanding the underlying framework of modifiers, domains, and core functions provides a foundation for navigating this changing landscape, regardless of how specific titles continue to evolve.

Looking ahead

As AI technology matures and becomes more integrated into standard business operations, expect job titles to stabilize around clearer functional distinctions. The current proliferation of creative combinations reflects a field still defining itself, but the underlying patterns of customer-facing versus product-focused work, and research versus application, will likely persist regardless of specific terminology.

For professionals entering the AI field, focusing on developing skills in the core functions—engineering, research, or solution design—while gaining familiarity with the major AI domains provides more career flexibility than optimizing for any particular title combination.